担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（研究会，シンポジウム資料等）  

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DOI： 10.1109/TCBB.2018.2814995

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s10265-018-1017-x

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：BioMed Central Ltd.  

Background: Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles. Results: Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift
1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods
three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed. Conclusions: Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.

DOI： 10.1186/s12859-018-2072-y

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：AMER MEDICAL ASSOC  

IMPORTANCE Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.
OBJECTIVE Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.
DESIGN, SETTING, AND PARTICIPANTS Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).
EXPOSURES Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.
MAIN OUTCOMES AND MEASURES The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.
RESULTS The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4%[95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P<.001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).
CONCLUSIONS AND RELEVANCE In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

DOI： 10.1001/jama.2017.14585

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記述言語：英語   出版者・発行元：IEEE Computer Society  

DOI： 10.1109/BIBE.2016.51

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IMPERIAL COLLEGE PRESS  

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

DOI： 10.1142/S0219720015430027

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

A similarity measure between statute books of local governments that can help reveal suggestive similarities is proposed. The regulations of a local government are stored in a statute book, and they are categorized in a layered structure. The layered structure can be described as an ordered tree in computer science, and we define the similarity of statute books as the tree edit distance between two trees. We have calculated the similarities among statute books of the 47 Japanese prefectures and plotted them on a plane using multi-dimensional scaling. The results visually indicate the relationships of similarities among them, and there are several outlier prefectures and clusters. This will help find local governments with similar regulations, which will facilitate the writing or revision of statutes, especially in small local governments, which are typically short staffed.

DOI： 10.1109/KSE.2015.57

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記述言語：英語  

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記述言語：英語  

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：PEERJ INC  

A vast amount of metagenomic data has been obtained by extracting multiple genomes simultaneously from microbial communities, including genomes from uncultivable microbes. By analyzing these metagenomic data, novel microbes are discovered and new microbial functions are elucidated. The first step in analyzing these data is sequenced-read classification into reference genomes from which each read can be derived. The Naive Bayes Classifier is a method for this classification. To identify the derivation of the reads, this method calculates a score based on the occurrence of a DNA sequence motif in each reference genome. However, large differences in the sizes of the reference genomes can bias the scoring of the reads. This bias might cause erroneous classification and decrease the classification accuracy. To address this issue, we have updated the Naive Bayes Classifier method usingmultiple sets of occurrence profiles for each reference genome by normalizing the genome sizes, dividing each genome sequence into a set of subsequences of similar length and generating profiles for each subsequence. This multiple profile strategy improves the accuracy of the results generated by the Naive Bayes Classifier method for simulated and Sargasso Sea datasets.

DOI： 10.7717/peerj.559

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

近年、バイオイメージング技術の進歩によって，様々な条件下での細胞や組織を高並列かつ長時間の動画として観察することが出来るようになっている．また様々なタンパク質の発現にそれぞれ異なる蛍光色素をプローブとしてカップリングする技術も進展しており，創薬研究における薬剤候補の選別や生命科学における表現型解析などへ応用されている．大量の動画から，様々な特徴量を抽出し細胞の特性の変化を定量的に解析するためには、自動的に精度良く細胞の追跡を行う必要がある．本研究では，複数種類の蛍光標識により多重染色された細胞をマルチチャンネルの蛍光顕微鏡で経時観察して得られる動画を対象として，パーティクルフィルタを用いた細胞の追跡手法を提案し，その有効性を評価するとともに得られた結果の考察を行う．

CiNii Books

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記述言語：日本語  

遺伝子は生命の遺伝情報を記録しており，互いに制御し合っていることが知られている．この複雑な遺伝子間の制御関係を解明するために，個々の遺伝子制御関係を統合した遺伝子制御ネットワークの推定が行われている．しかし遺伝子×実験条件の行列データである遺伝子発現プロファイルは遺伝子数に比べ1実験あたりの測定回数が少ない．数千，数万の遺伝子に対して測定は数十回程度しか行うことができず，測定回数が10以下の遺伝子発現プロファイルも多い．そのため遺伝子発現プロファイルの情報量不足を原因とした遺伝子制御ネットワーク推定の精度低下が問題点となる．本研究では情報量不足を軽減するために複数の時系列遺伝子発現プロファイルを利用し，全プロファイルで一貫して存在する共通の部分ネットワークを全プロファイルを用いて推定する．その後各実験条件で特徴的な部分ネットワークを共通する部分ネットワークをもとに各プロファイルを用いて推定することで推定精度の向上を目指す．本手法を実際の遺伝子制御ネットワークを基にしたシミュレーションデータと，網膜視細胞の桿体，錐体分化時の遺伝子発現プロファイルに対して適用し，有効性を明らかにした．Genes contain genetic information and regulate each other. Estimating gene regulatory networks reveals complicated regulations. However, the number of conditions or time points of gene expression profiles is fewer than that of genes. It causes degrading the estimation accuracy. In this study, we propose a robust method for estimating gene regulatory networks using multiple time series gene expression profiles. First, the proposed method estimates a common network under multiple conditions. Second, the common network is extended by adding characteristic regulations of each condition. We demonstrate the effectiveness of our method by applying it to in silico datasets and differentiation processes of mouse retina to rod and cone photoreceptors.

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記述言語：日本語  

様々な分子や細胞を可視化するバイオイメージング技術の発展は目覚ましく，医学・生物学研究を進めるうえでますます重要性を増している．またハイスループット化も進んでいるため，得られた画像・動画を自動的・客観的に解析するための情報処理技術の開発が喫緊の課題となっている．本研究では，蛍光タンパク質を用いて細胞周期の可視化を行ったイメージングデータから細胞核の自動検出と追跡を行うために，混合正規分布モデルを用いた方法を提案し，その有効性を評価するとともに得られた結果の考察を行う．Biological imaging technologies have been rapidly advancing for several years and have become an important part in the field of biology. Time-lapse imaging techniques enable the monitoring of multiple cellular functions using live cell assays. In order to process massive time-lapse images and perform quantitative analysis, automated image analysis (including cell segmentation and tracking trajectories, defining and retrieving various statistics) is required. In this study, we proposed a segmentation and tracking method of cell nuclei for time-lapse fluorescent microscopy images based on Gaussian mixture model.

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記述言語：英語  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Background: Identifying the differences between gene regulatory networks under varying biological conditions or external stimuli is an important challenge in systems biology. Several methods have been developed to reverse-engineer a cellular system, called a gene regulatory network, from gene expression profiles in order to understand transcriptomic behavior under various conditions of interest. Conventional methods infer the gene regulatory network independently from each of the multiple gene expression profiles under varying conditions to find the important regulatory relations for understanding cellular behavior. However, the inferred networks with conventional methods include a large number of misleading relations, and the accuracy of the inference is low. This is because conventional methods do not consider other related conditions, and the results of conventional methods include considerable noise due to the limited number of observation points in each expression profile of interest. Results: We propose a more accurate method for estimating key gene regulatory networks for understanding cellular behavior under various conditions. Our method utilizes multiple gene expression profiles that compose a tree structure under varying conditions. The root represents the original cellular state, and the leaves represent the changed cellular states under various conditions. By using this tree-structured gene expression profiles, our method more powerfully estimates the networks that are key to understanding the cellular behavior of interest under varying conditions. Conclusion: We confirmed that the proposed method in cell differentiation was more rigorous than the conventional method. The results show that our assumptions as to which relations are unimportant for understanding the differences of cellular states in cell differentiation are appropriate, and that our method can infer more accurately the core networks of the cell types. © 2012 Elsevier B.V.

DOI： 10.1016/j.gene.2012.11.090

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記述言語：英語   出版者・発行元：Japanese Society for Medical and Biological Engineering  

DOI： 10.11239/jsmbe.51.R-166

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記述言語：英語  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：BIOMED CENTRAL LTD  

Background: Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures.
Results: In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures.
Conclusions: We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

DOI： 10.1186/1471-2164-13-S1-S12

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Imperial College Press  

Background: Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures.
Results: In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures.
Conclusions: We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

DOI： 10.1186/1471-2164-13-S1-S12

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Alternative splicing plays an important role in eukaryotic gene expression by producing diverse proteins from a single gene. Predicting how genes are transcribed is of great biological interest. To this end, massively parallel whole transcriptome sequencing, often referred to as RNA-Seq, is becoming widely used and is revolutionizing the cataloging isoforms using a vast number of short mRNA fragments called reads. Conventional RNA-Seq analysis methods typically align reads onto a reference genome (mapping) in order to capture the form of isoforms that each gene yields and how much of every isoform is expressed from an RNA-Seq dataset. However, a considerable number of reads cannot be mapped uniquely. Those so-called multireads that are mapped onto multiple locations due to short read length and analogous sequences inflate the uncertainty as to how genes are transcribed. This causes inaccurate gene expression estimations and leads to incorrect isoform prediction. To cope with this problem, we propose a method for isoform prediction by iterative mapping. The positions from which multireads originate can be estimated based on the information of expression levels, whereas quantification of isoform-level expression requires accurate mapping. These procedures are mutually dependent, and therefore remapping reads is essential. By iterating this cycle, our method estimates gene expression levels more precisely and hence improves predictions of alternative splicing. Our method simultaneously estimates isoform-level expressions by computing how many reads originate from each candidate isoform using an EM algorithm within a gene. To validate the effectiveness of the proposed method, we compared its performance with conventional methods using an RNA-Seq dataset derived from a human brain. The proposed method had a precision of 66.7% and outperformed conventional methods in terms of the isoform detection rate.

DOI： 10.2197/ipsjtbio.5.27

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記述言語：英語   出版者・発行元：Information and Media Technologies Editorial Board  

Alternative splicing plays an important role in eukaryotic gene expression by producing diverse proteins from a single gene. Predicting how genes are transcribed is of great biological interest. To this end, massively parallel whole transcriptome sequencing, often referred to as RNA-Seq, is becoming widely used and is revolutionizing the cataloging isoforms using a vast number of short mRNA fragments called reads. Conventional RNA-Seq analysis methods typically align reads onto a reference genome (mapping) in order to capture the form of isoforms that each gene yields and how much of every isoform is expressed from an RNA-Seq dataset. However, a considerable number of reads cannot be mapped uniquely. Those so-called multireads that are mapped onto multiple locations due to short read length and analogous sequences inflate the uncertainty as to how genes are transcribed. This causes inaccurate gene expression estimations and leads to incorrect isoform prediction. To cope with this problem, we propose a method for isoform prediction by iterative mapping. The positions from which multireads originate can be estimated based on the information of expression levels, whereas quantification of isoform-level expression requires accurate mapping. These procedures are mutually dependent, and therefore remapping reads is essential. By iterating this cycle, our method estimates gene expression levels more precisely and hence improves predictions of alternative splicing. Our method simultaneously estimates isoform-level expressions by computing how many reads originate from each candidate isoform using an EM algorithm within a gene. To validate the effectiveness of the proposed method, we compared its performance with conventional methods using an RNA-Seq dataset derived from a human brain. The proposed method had a precision of 66.7% and outperformed conventional methods in terms of the isoform detection rate.

DOI： 10.11185/imt.7.921

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IMPERIAL COLLEGE PRESS  

The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses.

DOI： 10.1142/S0219720011005768

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：BIOMED CENTRAL LTD  

Background: With the advent of next-generation sequencers, the growing demands to map short DNA sequences to a genome have promoted the development of fast algorithms and tools. The tools commonly used today are based on either a hash table or the suffix array/Burrow-Wheeler transform. These algorithms are the best suited to finding the genome position of exactly matching short reads. However, they have limited capacity to handle the mismatches. To find n-mismatches, they requires O(2(n)) times the computation time of exact matches. Therefore, acceleration techniques are required.
Results: We propose a hash-based method for genome mapping that reduces the number of hash references for finding mismatches without increasing the size of the hash table. The method regards DNA subsequences as words on Galois extension field GF(2(2)) and each word is encoded to a code word of a perfect Hamming code. The perfect Hamming code defines equivalence classes of DNA subsequences. Each equivalence class includes subsequence whose corresponding words on GF(2(2)) are encoded to a corresponding code word. The code word is used as a hash key to store these subsequences in a hash table. Specifically, it reduces by about 70% the number of hash keys necessary for searching the genome positions of all 2-mismatches of 21-base-long DNA subsequence.
Conclusions: The paper shows perfect hamming code can reduce the number of hash references for hash-based genome mapping. As the computation time to calculate code words is far shorter than a hash reference, our method is effective to reduce the computation time to map short DNA sequences to genome. The amount of data that DNA sequencers generate continues to increase and more accurate genome mappings are required. Thus our method will be a key technology to develop faster genome mapping software.

DOI： 10.1186/1471-2164-12-S3-S8

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

多数のツールやサービスを組合せる必要がある生物情報解析を円滑に実行するためにワークフローが利用されている．しかし REST サービスは仕様を記述するための標準的な形式が確立されていないため，ワークフローツールでの利用が困難である．そこで本研究では，生物情報解析を行うワークフローツールで REST サービスを利用するための，SOAP サービスへの変換手法の提案を行う．提案手法では，REST サービスのドキュメントを URL 記載形式により分類し，各分類ごとに機械学習で REST サービスの URL を抽出する．抽出した URL をもとに REST サービスを中継する SOAP サービスを自動生成する．提案手法を BioCatalogue に登録されている REST サービスに適用し，その有効性の検証を行った．In bioinformatics, workflows have used frequently to combine many tools or services. But it have been difficult to use REST services by workflow tools, because there is no way to read their specification computationally. In this research, we proposed a method for using REST services in workflows by converting REST services to SOAP services. This method classfies documents of REST services by their forms writing URL. And by using machine learning, this method extracts URLs of only REST services from classified documents. By using extracted URLs, this method generates SOAP services that access REST services. To show effectiveness of this method, we use it with example REST services registered in BioCatalogue.

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：SPRINGER  

Peritoneal relapse is the most common pattern of tumor progression in advanced gastric cancer. Clinicopathological findings are sometimes inadequate for predicting peritoneal relapse. The aim of this study was to identify patients at high risk of peritoneal relapse in a prospective study based on molecular prediction.
RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly, we constructed a molecular prediction system and validated its robustness and prognostic validity by 500 times multiple validation by repeated random sampling in a retrospective set of 56 (38 relapse-free and 18 peritoneal-relapse) patients. Secondly, we applied this prediction to 85 patients of the prospective set to assess predictive accuracy and prognostic validity.
In the retrospective phase, repeated random validation yielded similar to 68% predictive accuracy and a 22-gene expression profile associated with peritoneal relapse was identified. The prediction system identified patients with poor prognosis. In the prospective phase, the molecular prediction yielded 76.9% overall accuracy. Kaplan-Meier analysis of peritoneal-relapse-free survival showed a significant difference between the "good signature group" and "poor signature group" (log-rank p = 0.0017). Multivariate analysis by Cox regression hazards model identified the molecular prediction as the only independent prognostic factor for peritoneal relapse.
Gene expression profile inherent to primary gastric cancer tissues can be useful in prospective prediction of peritoneal relapse after curative surgery, potentially allowing individualized postoperative management to improve the prognosis of patients with advanced gastric cancer.

DOI： 10.1245/s10434-009-0854-1

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記述言語：英語   出版者・発行元：Information and Media Technologies 編集運営会議  

To identify protein-protein interaction pairs with high accuracy, we propose a method for predicting these interactions based on characteristics obtained from protein-protein docking evaluations. Previous studies assumed that the required protein affinity strength for an interaction was not dependent on protein functions. However, the protein affinity strength appears to differ with different docking schemes, such as rigid-body or flexible docking, and these schemes may be related to protein functions. Thus, we propose a new scoring system that is based on statistical analysis of affinity score distributions sampled by their protein functions. As a result, of all possible protein pair combinations, a newly developed method improved prediction accuracy of F-measures. In particular, for bound antibody-antigen pairs, we obtained 50.0% recall (=sensitivity) with higher F-measures compared with previous studies. In addition, by combining two proposed scoring systems, Receptor-Focused Z-scoring and Ligand-Focused Z-scoring, further improvement was achieved. This result suggested that the proposed prediction method improved the prediction accuracy (i.e., F-measure), with few false positives, by taking biological functions of protein pairs into consideration.

DOI： 10.11185/imt.5.489

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

PubMed

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語   掲載種別：研究論文（その他学術会議資料等）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）   出版者・発行元：一般社団法人情報処理学会  

化合物データベースに登録されている化合物数は増加の一途をたどっている．化合物データベースの類似性検索では，構造キーと Tanimoto 係数を用いた検索がよく利用されるが，化合物数の増加に伴い検索にかかる時間が増加する．そこで本研究では，構造キーを分割することで類似性検索の計算範囲を絞り込む手法を提案し，実際の化合物データベースでの検索の際の計算回数を測定することによりその有効性を示した．The amount of compounds in public databases goes on increasing. A structure key and Tanimoto coefficient are often used for similarity searches in compound databases. As the number of compounds increases, the search time increases. In this research, we propose a method for refinement of the calculation range by divided structure key, and show the effectiveness by measuring the number of calculation with the data of database.

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その他リンク： http://id.nii.ac.jp/1001/00058866/

記述言語：英語   出版者・発行元：Information Processing Society of Japan  

The number of biological databases has been increasing rapidly as a result of progress in biotechnology. As the amount and heterogeneity of biological data increase, it becomes more difficult to manage the data in a few centralized databases. Moreover, the number of sites storing these databases is getting larger, and the geographic distribution of these databases has become wider. In addition, biological research tends to require a large amount of computational resources, i.e., a large number of computing nodes. As such, the computational demand has been increasing with the rapid progress of biological research. Thus, the development of methods that enable computing nodes to use such widely-distributed database sites effectively is desired. In this paper, we propose a method for providing data from the database sites to computing nodes. Since it is difficult to decide which program runs on a node and which data are requested as their inputs in advance, we have introduced the notion of "data-staging" in the proposed method. Data-staging dynamically searches for the input data from the database sites and transfers the input data to the node where the program runs. We have developed a prototype system with data-staging using grid middleware. The effectiveness of the prototype system is demonstrated by measurement of the execution time of similarity search of several-hundred gene sequences against 527 prokaryotic genome data.

DOI： 10.2197/ipsjdc.4.250

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記述言語：英語   出版者・発行元：Information Processing Society of Japan  

Chemical and biological activities of compounds provide valuable information for discovering new drugs. The compound fingerprint that is represented by structural information of the activities is used for candidates for investigating similarity. However, there are several problems with predicting accuracy from the requirement in the compound structural similarity. Although the amount of compound data is growing rapidly, the number of well-annotated compounds, e.g., those in the MDL Drug Data Report (MDDR)database, has not increased quickly. Since the compounds that are known to have some activities of a biological class of the target are rare in the drug discovery process, the accuracy of the prediction should be increased as the activity decreases or the false positive rate should be maintained in databases that have a large number of un-annotated compounds and a small number of annotated compounds of the biological activity. In this paper, we propose a new similarity scoring method composed of a combination of the Tanimoto coefficient and the proximity measure of random forest. The score contains two properties that are derived from unsupervised and supervised methods of partial dependence for compounds. Thus, the proposed method is expected to indicate compounds that have accurate activities. By evaluating the performance of the prediction compared with the two scores of the Tanimoto coefficient and the proximity measure, we demonstrate that the prediction result of the proposed scoring method is better than those of the two methods by using the Linear Discriminant Analysis (LDA) method. We estimate the prediction accuracy of compound datasets extracted from MDDR using the proposed method. It is also shown that the proposed method can identify active compounds in datasets including several un-annotated compounds.

DOI： 10.2197/ipsjdc.4.238

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記述言語：英語  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Japanese Society for Bioinformatics  

We present a new method to describe tissue-specific function that leverages the advantage of the Cap Analysis of Gene Expression (CAGE) data. The CAGE expression data represent the number of mRNAs of each gene in a sample. The feature enables us to compare or add the expression amount of genes in the sample. As usual methods compared the gene expression values among tissues for each gene respectively and ruled out to compare them among genes, they have not exploited the feature to reveal tissue specifi city. To utilize the feature, we used Gene Ontology terms (GO-terms) as unit to sum up the expression values and described specificities of tissues by them. We regard GO-terms as events that occur in the tissue according to probabilities that are defined by means of the CAGE. Our method is applied to mouse CAGE data on 22 tissues. Among them, we show the results of molecular functions and cellular components on liver. We also show the most expressed genes in liver to compare with our method. The results agree well with well-known specific functions such as amino acid metabolisms of liver. Moreover, the difference of inter-cellular junction among liver, lung, heart, muscle and prostate gland are apparently observed. The results of our method provide researchers a clue to the further research of the tissue roles and the deeper functions of the tissue-specific genes. All the results and supplementary materials are available via our web site.

DOI： 10.11234/gi1990.19.154

PubMed

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記述言語：日本語  

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記述言語：英語   出版者・発行元：Information Processing Society of Japan  

In bioinformatics, dealing with tools to analyze biological data becomes important. Those tools are provided by various institutions and the number of the tools is rapidly increasing. Recently many institutions have been offering those tools and access to databases with Web service technologies. The workflow technology is one of the ways to manage those tools and it is becoming available in bioinformatics. In order to compose workflows, several research groups develop and provide workflow composing tools. And consequently, the concept "Workflow Reuse" is also arisen in order to help workflow composition. Nevertheless it is still difficult to search for the reusable workflows from the repository of workflows in the current situation. In this paper, we propose a method to extract reusable workflows from the repository by using currently available information. We could extract some functionally similar workflows as reusable ones. By extracting reusable workflows efficiently, researchers can compose their workflow more easily.

DOI： 10.2197/ipsjdc.3.164

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

遺伝子発現プロファイルの蓄積にともない，遺伝子制御ネットワークの推定に，より多くの実験条件を含む発現プロファイルを用いることが可能になった．しかし，モジュールネットワークのような推定手法を，そのような発現プロファイルに対して適用すると，推定精度の低下を招く恐れがある．モジュールネットワークは，発現プロファイルの実験条件の大半で類似した発現を示す遺伝子が多数存在することを前提とするが，多くの実験条件を含む発現プロファイルほどそのような遺伝子は少ない．そこで本研究では，発現プロファイルのバイクラスタリングの結果を利用する．発現プロファイルの実験条件がバイクラスタに含まれる部分のみを用いて推定を行うことで，推定精度の低下の軽減を図る．本手法を出芽酵母の複数の発現プロファイルに対して適用し，有効性を検証した．The accumulation of gene-expression profiles can allow an inference of a gene regulatory network by using a profile measured under a number of experimental conditions. However, in case of applying to such profile, the conventional methods such as module network model may not perform an inference accurately enough, because of following two facts. 1) Module network can accurately perform an inference only for regulated genes that show similar gene-expression patterns under almost all experimental conditions. 2) In a gene-expression profile that includes more conditions, fewer genes show similar gene-expression patterns. To alleviate the accuracy loss, we utilized a biclustering result for an inference. We performed an inference for regulated genes that were included in a detected bicluster by using gene-expression patterns only under experimental conditions included in the bicluster. We demonstrate the effectiveness of our method by applying to inferences of gene regulatory networks by using various gene-expression profiles of budding yeast.

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その他リンク： http://id.nii.ac.jp/1001/00017157/

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：PUBLIC LIBRARY SCIENCE  

T he international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM(2), comprised 60,770 full- length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein- coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full- length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web- based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full- length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding ( including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full- length cDNAs. The total number of distinct non- protein- coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and. nal expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

DOI： 10.1371/journal.pgen.0020062

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：PUBLIC LIBRARY SCIENCE  

With the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. Moreover, in several cases, their transcripts, which control epigenetic transcription via processes such as transcriptional interference and genomic imprinting, occur in clusters. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns and that the characteristics of expression in the same genomic regions differ depending on tissue type and developmental stage. In this study, we analyzed gene expression patterns using the cap analysis gene expression ( CAGE) method for exploring systematic views of the mouse transcriptome. Counting the number of mapped CAGE tags for fixed-length regions allowed us to determine genomic expression levels. These expression levels were normalized, quantified, and converted into four types of descriptors, allowing the expression patterns along the genome to be represented by character strings. We analyzed them using dynamic programming in the same manner as for sequence analysis. We have developed a novel algorithm that provides a novel view of the genome from the perspective of genomic positional expression. In a similarity search of expression patterns across chromosomes and tissues, we found regions that had clusters of genes that showed expression patterns similar to each other depending on tissue type. Our results suggest the possibility that the regions that have sense - antisense transcription show similar expression patterns between forward and reverse strands.

DOI： 10.1371/journal.pgen.0020044

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：WORLD SCIENTIFIC PUBL CO PTE LTD  

With the rapid progress of the human genome project and related analyses, a huge amount of sequence data has been generated and a substantial number of methods has been proposed for predicting the potential functions based on sequence homology, functional patterns (motifs), domain information and so forth. It is often the case that actual processes of these biological analyses are not straightforward but rather complicated. In order to solve this problem, we propose a framework to virtualize and integrate various biological resources such as programs, databases and experimental data on the Grid environment. We show how our architecture makes it possible to improve the complicated process of biological analyses.

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：AMER ASSOC ADVANCEMENT SCIENCE  

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

DOI： 10.1126/science.1112014

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：OXFORD UNIV PRESS  

Motivation: Clustering of protein sequences is widely used for the functional characterization of proteins. However, it is still not easy to cluster distantly-related proteins, which have only regional similarity among their sequences. It is therefore necessary to develop an algorithm for clustering such distantly-related proteins.
Results: We have developed a time and space efficient clustering algorithm. It uses a graph representation where its vertices and edges denote proteins and their sequence similarities above a certain cutoff score, respectively. It repeatedly partitions the graph by removing edges that have small weights, which correspond to low sequence similarities. To find the appropriate partitions, we introduce a score combining the normalized cut and a locally minimal cut capacities. Our method is applied to the entire 40 703 human proteins in SWISS-PROT and TrEMBL. The resulting clusters shows a 76% recall (20 529 proteins) of the 26 917 classified by InterPro. It also finds relationships not found by other clustering methods.

DOI： 10.1093/bioinformatics/btg397

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：2  

Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relationships of genes as a tree structure by connecting genes using their similarity scores based on the Pearson correlation coefficient. But the clustering method is sensitive to experimental noise.<BR>To cope with the problem, we propose another type of clustering method (the <I>p</I>-quasi complete linkage clustering). We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods.

DOI： 10.11234/gi1990.15.2_151

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE COMPUTER SOC  

We propose a novel method for an exhaustive inference of gene regulatory networks from genome-wide expression data and biological knowledge. Our method performs the inferences based on module network model. In the model a module is a set of genes with similar features, and a network represents regulatory relationships among the modules. Our method makes modules using gene functional classification together with expression data. We apply our method to inferences of the networks of yeast cell-cycle. Modules inferred by our method show consistency with experimentally-determined results on yeast cell-cycle, especially on G1 phase. Robust modules built by our method permit us to infer informative regulatory relationships.

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT  

DOI： 10.1001/gr.1459703

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：SPRINGER-VERLAG BERLIN  

We present a method to shorten the computational time of the fluorescent DNA computing. Fluorescent DNA computing is proposed to solve intractable computation problems such as SAT problems. They use two groups of fluorescent DNA strands. One group of fluorescent DNA represents that a constraint of the given problem is satisfied, and another group represents that a constraint is unsatisfied. The calculation is executed by hybridizing them competitively to DNA beads or spots on DNA microarray. Though the biological operation used in the fluorescent DNA computing is simple, it needs the same number of beads or spots on microarray as the number of candidate solutions. In this paper, we prove that one bead or spot can represent plural candidate solutions through SAT problem, and show the algorithm and an experimental result of the fluorescent DNA computing.

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE COMPUTER SOC  

This paper presents a clustering method for the comparative analysis of genomes and metabolic pathways. Our method consists of several steps: (1) extract linear sequences of metabolic reactions (non-branching pathways) from a network of metabolic pathways, (2) perform pairwise alignments of every pair of nonbranching pathways, and (3) explore a set of genes for each aligned non-branching pathways such that those genes encode enzymes of the pathway and the genes are closely located on a genome (such as operons or gene clusters). To measure the similarity between pathways, we formalized a scoring system by using the functional hierarchy of the EC numbers of enzymes. By applying our method to the metabolic pathways in Escherichia coli, we have obtained several Pairs of pathways with similar reactions having enzymes encoded by neighbor genes on the E. coli genome. The resulting pathway pairs were mainly found in metabolisms between similar amino acids (tryptophan and histidine) and between similar sugars (fucose and rhamnose).

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：NATURE PUBLISHING GROUP  

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 &apos;transcriptional units&apos;, contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

DOI： 10.1038/nature01266

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記述言語：英語   出版者・発行元：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

A constraint resolution scheme in the Hopfield-type neural network named "Neuron Filter" is presented for efficiently solving combinatorial optimization problems. The neuron filter produces an output that satisfies the constraints of the problem as best as possible according to both neuron inputs and outputs. This paper defines the neuron filter and shows its introduction into existing neural networks for N-queens problems and FPGA board-level routing problems. The performance is evaluated through simulations wharf the results show that our neuron filter improves the searching: capability of the neural network with the shorter computation time.

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Scripta Technica Inc  

In this paper, we propose a neural network algorithm that uses the expanded maximum neuron model to solve the channel assignment problem of cellular radio networks, which is an NP-complete combinatorial optimization problem. The channel assignment problem demands minimizing the total interference between the assigned channels needed to satisfy all of the communication needs. The proposed expanded maximum neuron model selects multiple neurons in descending order from the neuron inputs in each neuron group. As a result, the constraints will always be satisfied for the channel assignment problem. To improve the accuracy of the solution, neuron fixing, which is a heuristic technique used in the binary neuron model, a hill-climbing term, a shaking term, and an Omega function are introduced. The effectiveness of these additions to the expanded maximum neuron model algorithm is demonstrated. Simulations of benchmark problems demonstrate the superior performance of the proposed algorithm over conventional algorithms in finding the solution.

DOI： 10.1002/(SICI)1520-6440(200011)83:11<11::AID-ECJC2>3.0.CO;2-D

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Verlag  

A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N × N chessboard such that no pair of queens commands each other. The maximum rjeuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N2-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

DOI： 10.1007/s004220050337

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

複数の遺伝子間で行われている転写制御関係をグラフにより可視化したものを遺伝子制御ネットワークと呼ぶ．遺伝子制御ネットワークの全容は未だ解明されておらず，計算機によって推定することができれば医学，創薬の分野において大幅に実験コストを削減できる．その為，遺伝子制御ネットワークの推定手法の研究はバイオインフォマティクスにおいて非常に重要なテーマである．遺伝子制御ネットワークの解析手法の一つに，ベイジアンネットワークがある．このモデルは他のモデルに比べノイズに強く，データ数が少ない場合にも推定が可能である．しかしこのモデルはデータ数が増えると爆発的に探索空間が大きくなるため，探索を行うには非常に大きな計算量が必要となる．この欠点を回避するため，近似法であるグリーディ法を用いることが多い．遺伝子制御ネットワークを推定する研究では，ネットワークを推定した後に生物学実験や生物学者の議論の結果より得られた制御関係を考慮して反復的にネットワーク推定を行う．推定結果の反復構築とは，ネットワークを推定した後に新たな制御関係に応じてネットワークを修正するためにこれを考慮してネットワークを再度推定することである．しかし反復構築を行う場合，反復回数に比例して計算量が必要となる．本研究では，グリーディ法で反復的に推定結果を構築する場合における計算量の軽減のための手法を提案する．反復的な推定のために構築された結果のさらなる利用により計算量の軽減を図り，従来の反復推定の手法と比較することでその有効性を検証した．

CiNii Books

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

複数の遺伝子間で行われている転写制御関係をグラフにより可視化したものを遺伝子制御ネットワークと呼ぶ．遺伝子制御ネットワークの全容は未だ解明されておらず，計算機によって推定することができれば医学，創薬の分野において大幅に実験コストを削減できる．その為，遺伝子制御ネットワークの推定手法の研究はバイオインフォマティクスにおいて非常に重要なテーマである．遺伝子制御ネットワークの解析手法の一つに，ベイジアンネットワークがある．このモデルは他のモデルに比べノイズに強く，データ数が少ない場合にも推定が可能である．しかしこのモデルはデータ数が増えると爆発的に探索空間が大きくなるため，探索を行うには非常に大きな計算量が必要となる．この欠点を回避するため，近似法であるグリーディ法を用いることが多い．遺伝子制御ネットワークを推定する研究では，ネットワークを推定した後に生物学実験や生物学者の議論の結果より得られた制御関係を考慮して反復的にネットワーク推定を行う．推定結果の反復構築とは，ネットワークを推定した後に新たな制御関係に応じてネットワークを修正するためにこれを考慮してネットワークを再度推定することである．しかし反復構築を行う場合，反復回数に比例して計算量が必要となる．本研究では，グリーディ法で反復的に推定結果を構築する場合における計算量の軽減のための手法を提案する．反復的な推定のために構築された結果のさらなる利用により計算量の軽減を図り，従来の反復推定の手法と比較することでその有効性を検証した．

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記述言語：英語   出版者・発行元：一般社団法人情報処理学会  

Genome analyses using short reads which are generated by the high-throughput sequencer begin by mapping reads to the target genome.Therefore, high accuracy mapping is crucial. However, conventional mapping tools cause multireads and unmapped reads, that is, the mapping accuracy is insufficient. One reason for causing those reads is that only one genome sequence is used as the reference, even if the targetorganisms are polyploidy. We propose a novel mapping method that takes alleles into account for the purpose of reducing multireads and unmapped reads, that is, improving mapping accuracy.

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記述言語：英語   出版者・発行元：情報処理学会  

CiNii Books

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記述言語：日本語  

遺伝子の機能は研究され, 遺伝子に付加される機能情報は日々増加し続けている．こうした遺伝子機能情報を利用した解析手法の一つに, 2つの実験条件の比較を目的とした遺伝子機能グループ解析がある．しかし, この解析手法では遺伝子機能の時間変化を解析することができない．そこで本研究では, 時系列データをスライディングウィンドウ方式で分割し, すべての分割期間に対して遺伝子機能グループ解析手法を実行することで時系列に対応できる遺伝子機能グループ解析を提案する．その結果, ある遺伝子機能が特定の期間で有意に発現していることを示した．Gene function is researched and gene functional information which is annotated on gene is increasing continuously. Gene Set Analysis is one of a method using gene functional information, and we use it when we want to compare two groups. However, this method can not be applied to time-series gene expression profile. In this reserch, I propose a method to analyze gene function groupsand handle time-series data. The method extracts a time period in which works from a time-series gene expression profile.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

遺伝子の機能は研究され,遺伝子に付加される機能情報は日々増加し続けている.こうした遺伝子機能情報を利用した解析手法の一つに,2つの実験条件の比較を目的とした遺伝子機能グループ解析がある.しかし,この解析手法では遺伝子機能の時間変化を解析することができない.そこで本研究では,時系列データをスライディングウィンドウ方式で分割し,すべての分割期間に対して遺伝子機能グループ解析手法を実行することで時系列に対応できる遺伝子機能グループ解析を提案する.その結果,ある遺伝子機能が特定の期間で有意に発現していることを示した.

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記述言語：英語   出版者・発行元：一般社団法人電子情報通信学会  

Alternative splicing plays an important role in eukaryotic gene expression by producing diverse proteins from a single gene. Predicting how genes are transcribed is of great biological interest. To this end, massively parallel whole transcriptome sequencing, often referred to as RNA-Seq, is becoming widely used and is revolutionizing the cataloging isoforms using a vast number of short mRNA fragments called reads. Conventional RNA-Seq analysis methods typically align reads onto a reference genome (mapping) in order to capture the form of isoforms that each gene yields and how much of every isoform is expressed from an RNA-Seq dataset. However, a considerable number of reads cannot be mapped uniquely. Those so-called multireads that are mapped onto multiple locations due to short read length and analogous sequences inflate the uncertainty as to how genes are transcribed. This causes inaccurate gene expression estimations and leads to incorrect isoform prediction. To cope with this problem, we propose a method for isoform prediction by iterative mapping. The positions from which multireads originate can be estimated based on the information of expression levels, whereas quantification of isoform-level expression requires accurate mapping. These procedures are mutually dependent, and therefore remapping reads is essential. By iterating this cycle, our method estimates gene expression levels more precisely and hence improves predictions of alternative splicing. Our method simultaneously estimates isoform-level expressions by computing how many reads originate from each candidate isoform using an EM algorithm within a gene. To validate the effectiveness of the proposed method, we compared its performance with conventional methods using an RNA-Seq dataset derived from a human brain. The proposed method had a precision of 66.7% and outperformed conventional methods in terms of the isoform detection rate.

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記述言語：英語   出版者・発行元：一般社団法人情報処理学会  

Alternative splicing plays an important role in eukaryotic gene expression by producing diverse proteins from a single gene. Predicting how genes are transcribed is of great biological interest. To this end, massively parallel whole transcriptome sequencing, often referred to as RNA-Seq, is becoming widely used and is revolutionizing the cataloging isoforms using a vast number of short mRNA fragments called reads. Conventional RNA-Seq analysis methods typically align reads onto a reference genome (mapping) in order to capture the form of isoforms that each gene yields and how much of every isoform is expressed from an RNA-Seq dataset. However, a considerable number of reads cannot be mapped uniquely. Those so-called multireads that are mapped onto multiple locations due to short read length and analogous sequences inflate the uncertainty as to how genes are transcribed. This causes inaccurate gene expression estimations and leads to incorrect isoform prediction. To cope with this problem, we propose a method for isoform prediction by iterative mapping. The positions from which multireads originate can be estimated based on the information of expression levels, whereas quantification of isoform-level expression requires accurate mapping. These procedures are mutually dependent, and therefore remapping reads is essential. By iterating this cycle, our method estimates gene expression levels more precisely and hence improves predictions of alternative splicing. Our method simultaneously estimates isoform-level expressions by computing how many reads originate from each candidate isoform using an EM algorithm within a gene. To validate the effectiveness of the proposed method, we compared its performance with conventional methods using an RNA-Seq dataset derived from a human brain. The proposed method had a precision of 66.7% and outperformed conventional methods in terms of the isoform detection rate.

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記述言語：日本語  

本研究では高速シーケンサが出力するショートリードを用いて DNA 塩基配列構造の有向非循環グラフ表現を提案する．解析対象となる DNA を断片的に読み取ったショートリードはサンプル特有の塩基配列構造情報を内在している．それらを抽出するためにショートリードクラスタリングが行われるが，従来のクラスタの表示法では構造情報の抽出が難しい．そこで我々は近隣リードを考慮したショートリードクラスタリングを行い，クラスタを有向非循環グラフで表現することで，構造情報の効果的な抽出を目指す．We propose a method to describe DNA sequence structures as direct acyclic graphs from short reads generated by high-throughput sequencer. The sequenced DNA fragments are called the short reads and they inherent sequence structures. Although short read clustering has developed to extract the sequence structures, current description of the clusters is less applicable to it. Therefore we first operate clustering with neighboring reads, and convert the clusters into direct acyclic graph in order to achieve effective extraction of sequence structures.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

多数のツールやサービスを組合せる必要がある生物情報解析を円滑に実行するためにワークフローが利用されている.しかしRESTサービスは仕様を記述するための標準的な形式が確立されていないため,ワークフローツールでの利用が困難である.そこで本研究では,生物情報解析を行うワークフローツールでRESTサービスを利用するための,SOAPサービスへの変換手法の提案を行う.提案手法では,RESTサービスのドキュメントをURL記載形式により分類し,各分類ごとに機械学習でRESTサービスのURLを抽出する.抽出したURLをもとにRESTサービスを中継するSOAPサービスを自動生成する.提案手法をBio Catalogueに登録されているRESTサービスに適用し,その有効性の検証を行った.

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記述言語：日本語   出版者・発行元：情報処理学会  

医薬品開発の大きな課題の一つとして，新たな薬の候補となる化合物を効果的に発見するため，化合物集合の中から特定のタンパク質に作用する可能性のある候補化合物を計算機によって探索する過程がある．候補化合物の探索には化合物の構造類似性が用いられていることが多いがデータベースに登録されている化合物量が日々増加しており，類似化合物検索の高速化が必要とされている．本研究では，化合物の部分構造情報を数値化した構造キーと，その類似尺度の一つとして Tanimoto 係数を用いた高速な類似化合物検索方法を提案する．提案手法では，化合物集合をクラスタリングするより類似化合物検索を高速化する．また，提案手法を従来手法と比較し，提案手法を評価する．In order to find compounds as candidates for drugs, one of the major problems of drug development, it is necessary to find compounds that might affect the proteins by using computer. Analysis of the compound structure similarity is a widely used method to discover candidate compounds. Nowadays, as the volume of compounds database is becoming rapidly increased, it is important to accelerate the compounds searching. In this paper, we propose a method to speed up compound searching by using structure key and Tanimoto coefficient. We use some methods for grouping similar compounds and evaluate our method by comparing it with the algorithm normally used.

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その他リンク： http://id.nii.ac.jp/1001/00068046/

記述言語：英語  

To identify protein-protein interaction pairs with high accuracy, we propose a method for predicting these interactions based on characteristics obtained from protein-protein docking evaluations. Previous studies assumed that the required protein affinity strength for an interaction was not dependent on protein functions. However, the protein affinity strength appears to differ with different docking schemes, such as rigid-body or flexible docking, and these schemes may be related to protein functions. Thus, we propose a new scoring system that is based on statistical analysis of affinity score distributions sampled by their protein functions. As a result, of all possible protein pair combinations, a newly developed method improved prediction accuracy of F-measures. In particular, for bound antibody-antigen pairs, we obtained 50.0% recall (= sensitivity) with higher F-measures compared with previous studies. In addition, by combining two proposed scoring systems, Receptor-Focused Z-scoring and Ligand-Focused Z-scoring, further improvement was achieved. This result suggested that the proposed prediction method improved the prediction accuracy (i.e., F-measure), with few false positives, by taking biological functions of protein pairs into consideration. © 2010 Information Processing Society of Japan.

DOI： 10.2197/ipsjtbio.3.10

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記述言語：英語  

To identify protein-protein interaction pairs with high accuracy, we propose a method for predicting these interactions based on characteristics obtained from protein-protein docking evaluations. Previous studies assumed that the required protein affinity strength for an interaction was not dependent on protein functions. However, the protein affinity strength appears to differ with different docking schemes, such as rigid-body or flexible docking, and these schemes may be related to protein functions. Thus, we propose a new scoring system that is based on statistical analysis of affinity score distributions sampled by their protein functions. As a result, of all possible protein pair combinations, a newly developed method improved prediction accuracy of F-measures. In particular, for bound antibody-antigen pairs, we obtained 50.0% recall (= sensitivity) with higher F-measures compared with previous studies. In addition, by combining two proposed scoring systems, Receptor-Focused Z-scoring and Ligand-Focused Z-scoring, further improvement was achieved. This result suggested that the proposed prediction method improved the prediction accuracy (i.e., F-measure), with few false positives, by taking biological functions of protein pairs into consideration. © 2010 Information Processing Society of Japan.

DOI： 10.2197/ipsjtbio.3.10

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記述言語：日本語  

タンパク質,化合物データベース等を用いて創薬のための化合物探索を行う際には,複数の大規模な創薬関連データベースを探索しなければならない.しかしながら,データベースの容量が膨大であるため単純に検索すると組合せ爆発を起こす恐れがある.そこで,本研究では,複数の創薬関連データベースを用いて,ターゲットタンパク質から相互作用する化合物を,組合せ爆発を抑えながら探索する手法を提案する.具体的には,ターゲットタンパク質から相同なタンパク質集合を探索し,相互作用情報と化合物探索手法によって,相互作用する化合物を推定する.本手法を既知のタンパク質・タンパク質-化合物相互作用,化合物のデータベース群に対して評価実験を行い,有用性を示す.

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

化合物データベースに登録されている化合物数は増加の一途をたどっている．化合物のタンパク質への作用は，化合物の構造に因るところが大きいことから，化合物のデータを単純に蓄積するだけではなく，クラスタリング等により構造情報に基づいたデータの整理が必要である．その際，化合物間で類似度を計算する必要があるが，化合物数の増加に伴い，類似度計算にかかる時間が増加する．そこで本研究では，Tanimoto係数の性質を利用し，類似度計算回数を削減する手法を提案し，化合物データを用いた実験によりその有効性を示した．The amount of compounds in public databases goes on increasing. It is necessary not only to accumulate compound data but also to classify them such as clustering based on their structures, because their activities on proteins depend on the structures. It is necessary to calculate the similarity between compounds, but as the number of compounds increases, the calculation time increases. In this research, we propose a method for reducing the number of calculating similarity based on the property of the Tanimoto coefficient, and show the effectiveness by the experiment with compound data.

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その他リンク： http://id.nii.ac.jp/1001/00058900/

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記述言語：英語   出版者・発行元：SPRINGER HEIDELBERG  

Gene expressions differ depending on tissue types and developmental stages. Analyzing how each gene is expressed is thus important. One way of analyzing gene expression patterns is to identify tissue-specific functions. This is useful for understanding how vital activities are performed. DNA microarray has been widely used to observe gene expressions exhaustively. However, comparing the expression value of a gene to that of other genes is impossible, as the gene expression value of a condition is measured as a proportion of that for the same gene under a control condition. We therefore could not determine whether one gene is more expressed than other genes. Cap analysis gene expression (CAGE) allows high-throughput analysis of gene expressions by counting the number of cDNAs of expressed genes. CAGE enables comparison of the expression value of the gene to that of other genes in the same tissue. In this study, we propose a method for exploring tissue-specific functions using data from CAGE. To identify tissue-specificity, one of the simplest ways is to assume that the function of the most expressed gene is regarded as the most tissue-specific. However, the most expressed gene in a tissue might highly express in all tissues, as seen with housekeeping genes. Functions of such genes cannot be tissue-specific. To remove these from consideration, we propose measuring tissue specificity of functions based on information content of gene ontology terms. We applied our method to data from 16 human tissues and 22 mouse tissues. The results from liver and prostate gland indicated that well-known functions of these tissues, such as functions related to signaling and muscle in prostate gland and immune function in liver, displayed high rank.

DOI： 10.1007/s11517-007-0274-y

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記述言語：英語   出版者・発行元：SPRINGER HEIDELBERG  

Gene expressions differ depending on tissue types and developmental stages. Analyzing how each gene is expressed is thus important. One way of analyzing gene expression patterns is to identify tissue-specific functions. This is useful for understanding how vital activities are performed. DNA microarray has been widely used to observe gene expressions exhaustively. However, comparing the expression value of a gene to that of other genes is impossible, as the gene expression value of a condition is measured as a proportion of that for the same gene under a control condition. We therefore could not determine whether one gene is more expressed than other genes. Cap analysis gene expression (CAGE) allows high-throughput analysis of gene expressions by counting the number of cDNAs of expressed genes. CAGE enables comparison of the expression value of the gene to that of other genes in the same tissue. In this study, we propose a method for exploring tissue-specific functions using data from CAGE. To identify tissue-specificity, one of the simplest ways is to assume that the function of the most expressed gene is regarded as the most tissue-specific. However, the most expressed gene in a tissue might highly express in all tissues, as seen with housekeeping genes. Functions of such genes cannot be tissue-specific. To remove these from consideration, we propose measuring tissue specificity of functions based on information content of gene ontology terms. We applied our method to data from 16 human tissues and 22 mouse tissues. The results from liver and prostate gland indicated that well-known functions of these tissues, such as functions related to signaling and muscle in prostate gland and immune function in liver, displayed high rank.

DOI： 10.1007/s11517-007-0274-y

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

代謝ネットワーク中でどの部分の反応経路間で類似が見られるかは，生物が進化の過程でどのように代謝ネットワークを構築してきたかを明らかにする上で重要な情報である先行研究として．代謝ネットワーク中における反応の類似性を，反応を触媒する酵素に付けられたＥＣ番号を利用して定義し，類似反応列を抽出することが行われてきた．しかしＥＣ番号が全く異なる反応間においても，反応前後の化合物の構造変化が類似している反応の組も多く存在する．そこで本研究では反応間の化合物の構造変化によって定義された反応識別番号を用いて，ネットワーク中の類似性を抽出する手法を提案する．Finding a common pattern among metabolic network gives important information on the evolution of the pathway. In previous works, it has been done to define the similarity of the reactions in the metabolic network by using the EC number. There are many reactions whose conformational changes of the compounds are similar, though their EC numbers are quite different. In this research, we propose a method for extracting the similarity in the network by using Reaction Classification Number.

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その他リンク： http://id.nii.ac.jp/1001/00058990/

記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

代謝反応パスウェイは組織によって働き方が異なる．この違いから遺伝子の機能解析に重要な情報を得ることができると考えられている．しかし組織特徴的な酵素をマッピングしたところ パスウェイ上で必ずしも連続的な位置を占めなかった．理由の一つとして 全組織で同じような発現量を示す遺伝子が間に存在することが挙げられる．このような遺伝子で断片化されたパスウェイは一体として働いていると考えられる．本研究では 絶対値発現量を用いて外れ値解析を行うことにより，連続した組織特徴的なパスウェイを抽出する手法を提案する．This article presents a method to extract tissue-specific metabolic-pathways from absolute expression data based on outlier detection. By mapping tissue-specific genes to metabolic pathways, majority of ac quired pathways are in flagments. It is caused by existance of non-tissue-specific genes between small fragments, which are considered to work with a unit-We defined a score from expression profile of a path way and gene which are included in the pathway?

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その他リンク： http://id.nii.ac.jp/1001/00058988/

記述言語：英語   出版者・発行元：SPRINGER-VERLAG BERLIN  

We propose a computational inference of transcriptional regulatory networks from transcription start sites identified by Cap Analysis of Gene Expression (CAGE). Inference of the regulatory networks is a challenging task, and is performed by analyzing observed dependencies of transcription levels. Binding of transcription factors to an upstream sequence of a gene enables transcription initiation from a specific genomic position. The position is called Transcription Start Site (TSS). Eukaryotes have multiple TSSs for a single gene, which reflect diversity in combinatorial regulation by transcription factors. By the CAGE high-throughput profiling genome-wide identification of activated TSSs under various experimental conditions yields CAGE transcriptome datasets. To distinguish transcription responses caused by the diversity in combinatorial regulations, inference that takes multiple TSSs into account will be more effective than the conventional ones using only transcription levels of microarray dataset.
In this paper we report a feasibility study of inference of transcriptional regulatory network by using CAGE dataset. This is a preliminary study for inference that takes multiple TSSs into account. To perform inference of the regulatory networks, we used Bayesian network model which is appropriate to represent distribution of TSSs observed in CAGE dataset. Using the CAGE dataset published from the FANTOM3 project in RIKEN, we applied the model to inference of the regulatory networks of Mus musculus. For the purpose of the feasibility study, we compared inferred networks from the CAGE dataset with those from microarray dataset. Based on the comparative analysis, we confirmed that network inference by using the CAGE dataset is feasible like the case of conventional inferences by using microarray dataset. Based on the confirmation we discussed what we can reveal through the inference that takes into account multiple TSSs identified by a CAGE dataset.

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記述言語：英語   出版者・発行元：SPRINGER-VERLAG BERLIN  

We propose a computational inference of transcriptional regulatory networks from transcription start sites identified by Cap Analysis of Gene Expression (CAGE). Inference of the regulatory networks is a challenging task, and is performed by analyzing observed dependencies of transcription levels. Binding of transcription factors to an upstream sequence of a gene enables transcription initiation from a specific genomic position. The position is called Transcription Start Site (TSS). Eukaryotes have multiple TSSs for a single gene, which reflect diversity in combinatorial regulation by transcription factors. By the CAGE high-throughput profiling genome-wide identification of activated TSSs under various experimental conditions yields CAGE transcriptome datasets. To distinguish transcription responses caused by the diversity in combinatorial regulations, inference that takes multiple TSSs into account will be more effective than the conventional ones using only transcription levels of microarray dataset.
In this paper we report a feasibility study of inference of transcriptional regulatory network by using CAGE dataset. This is a preliminary study for inference that takes multiple TSSs into account. To perform inference of the regulatory networks, we used Bayesian network model which is appropriate to represent distribution of TSSs observed in CAGE dataset. Using the CAGE dataset published from the FANTOM3 project in RIKEN, we applied the model to inference of the regulatory networks of Mus musculus. For the purpose of the feasibility study, we compared inferred networks from the CAGE dataset with those from microarray dataset. Based on the comparative analysis, we confirmed that network inference by using the CAGE dataset is feasible like the case of conventional inferences by using microarray dataset. Based on the confirmation we discussed what we can reveal through the inference that takes into account multiple TSSs identified by a CAGE dataset.

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記述言語：英語   出版者・発行元：PUBLIC LIBRARY SCIENCE  

T he international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM(2), comprised 60,770 full- length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein- coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full- length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web- based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full- length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding ( including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full- length cDNAs. The total number of distinct non- protein- coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and. nal expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

DOI： 10.1371/journal.pgen.0020062

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記述言語：英語   出版者・発行元：PUBLIC LIBRARY SCIENCE  

T he international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM(2), comprised 60,770 full- length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein- coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full- length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web- based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full- length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding ( including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full- length cDNAs. The total number of distinct non- protein- coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and. nal expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

DOI： 10.1371/journal.pgen.0020062

Web of Science

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

CiNii Books

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

CiNii Books

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

遺伝子発現プロファイルの蓄積にともない，遺伝子制御ネットワークの推定に，より多くの実験条件を含む発現プロファイルを用いることが可能になった．しかし，モジュールネットワークのような推定手法を，そのような発現プロファイルに対して適用すると，推定精度の低下を招く恐れがある．モジュールネットワークは，発現プロファイルの実験条件の大半で類似した発現を示す遺伝子が多数存在することを前提とするが，多くの実験条件を含む発現プロファイルほどそのような遺伝子は少ない．そこで本研究では，発現プロファイルのバイクラスタリングの結果を利用する．発現プロファイルの実験条件がバイクラスタに含まれる部分のみを用いて推定を行うことで，推定精度の低下の軽減を図る．本手法を出芽酵母の複数の発現プロファイルに対して適用し，有効性を検証した．The accumulation of gene-expression profiles can allow an inference of a gene regulatory network by using a profile measured under a number of experimental conditions. However, in case of applying to such profile, the conventional methods such as module network model may not perform an inference accurately enough, because of following two facts. 1) Module network can accurately perform an inference only for regulated genes that show similar gene-expression patterns under almost all experimental conditions. 2) In a gene-expression profile that includes more conditions, fewer genes show similar gene-expression patterns. To alleviate the accuracy loss, we utilized a biclustering result for an inference. We performed an inference for regulated genes that were included in a detected bicluster by using gene-expression patterns only under experimental conditions included in the bicluster. We demonstrate the effectiveness of our method by applying to inferences of gene regulatory networks by using various gene-expression profiles of budding yeast.

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その他リンク： http://id.nii.ac.jp/1001/00017157/

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

年々増加する代謝ネットワークの情報から生命機能情報を抽出するための方法について注目が集まりつつある．本研究では，代謝ネットワークから生物種間で保存されたサブネットワークを網羅的に探索する手法を開発した．本手法では代謝ネットワークをグラフとして扱い，グラフ中で隣接した酵素反応間の類似スコアを系統プロファイルに基づいて定義する．探索アルゴリズムは深さ優先グラフ探索に基づいている．19の生物種についてKEGGのデータを本手法に適用したところ，ほとんどの生物種に保存されたサブネットワーク，および古細菌とバクテリアで保存されたトリプトファン生合成系パスウェイが結果として得られた．This article presents a method to extract metabolic sub-networks conserved among organisms based on similarity between phylogenetic profiles of enzymatic reactions. We formulated a profile similarity score based on the phylogenetic profile. By using the score, we perform a connected component search algorithm. By applying our approach to the metabolic networks of 19 representative organisms selected from bacteria, archaea, and eukaryotes in the KEGG database, we detected some highly conserved sub-networks among the organisms. We obtained one of hte sub-network conserved among most of the organisms and the tryptophan biosynthesis pathway conserved between bacteria and archaea.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00059080/

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

遺伝子間の制御関係は遺伝子の制御ネットワークとしてグラフによって表現する事ができる. 性質の似た変数の集合をモジュールとして扱うModule Bayesian Networkを用いて 遺伝子の制御ネットワークを推定する研究が行われているが 遺伝子の数に対して測定結果の数が不足しているため正確な推定を行えていない. この様な場合 推定に事前知識を採り入れる事が有効とされるが Module Bayesian Networkを用いた過去の研究では モジュールの推定に十分な事前知識を採り入れていない. そこで本研究では Gene Ontologyを用いた遺伝子に対する注釈情報を利用して 注釈の似た遺伝子を同一のモジュールに集めるモジュール評価関数を提案する. 本手法を実際の遺伝子間の制御関係の推定に適用し 性能評価を行った.The gene regulation networks are directed graph expression of gene regulations. Because the amount of expression profile's samples is rarely enough to robustly learn dependencies of a large number of genes, any methods have not succeeded in a precise inference of gene regulation network. In this research a gene regulation network is infered by modeling to Module Bayesian Networks, which are introduced the notion of module in which variables set has the same dependency, and a module scoring method is proposed so that infered module's genes have the similar annotation of prior knowledge. A performance of proposed method is evaluated on the actual gene expression profile.

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その他リンク： http://id.nii.ac.jp/1001/00033354/

記述言語：英語  

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記述言語：英語  

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記述言語：英語   出版者・発行元：SPRINGER-VERLAG BERLIN  

We present a method to shorten the computational time of the fluorescent DNA computing. Fluorescent DNA computing is proposed to solve intractable computation problems such as SAT problems. They use two groups of fluorescent DNA strands. One group of fluorescent DNA represents that a constraint of the given problem is satisfied, and another group represents that a constraint is unsatisfied. The calculation is executed by hybridizing them competitively to DNA beads or spots on DNA microarray. Though the biological operation used in the fluorescent DNA computing is simple, it needs the same number of beads or spots on microarray as the number of candidate solutions. In this paper, we prove that one bead or spot can represent plural candidate solutions through SAT problem, and show the algorithm and an experimental result of the fluorescent DNA computing.

Web of Science

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記述言語：英語   出版者・発行元：NATURE PUBLISHING GROUP  

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 &apos;transcriptional units&apos;, contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

DOI： 10.1038/nature01266

Web of Science

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記述言語：英語   掲載種別：速報，短報，研究ノート等（学術雑誌）   出版者・発行元：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.

Web of Science

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記述言語：英語   出版者・発行元：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

A constraint resolution scheme in the Hopfield-type neural network named "Neuron Filter" is presented for efficiently solving combinatorial optimization problems. The neuron filter produces an output that satisfies the constraints of the problem as best as possible according to both neuron inputs and outputs. This paper defines the neuron filter and shows its introduction into existing neural networks for N-queens problems and FPGA board-level routing problems. The performance is evaluated through simulations wharf the results show that our neuron filter improves the searching: capability of the neural network with the shorter computation time.

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記述言語：英語   出版者・発行元：一般社団法人電子情報通信学会  

本論文では, 論理エミュレーションシステムにおけるFPGA間の配線問題(BLRP)に対するニューラルネットワーク解法において, ノンフィードバック・ニューロンフィルタの提案を行う.部分機能を担う複数のFPGAの相互結合による論理エミュレーションシステムは, 大規模デジタルシステムの開発機関の短縮を可能とする.BLRPとは, 与えられたネットリスクを基にFPGA間ネットのクロスバースイッチを介した配線配置を行う, NP完全クラスに属する問題組合せ最適化である.本論文では, BLRPに対するニューロンフィルタアルゴリズム, 及び, ニューロンフィルタの新しい適用方法の提案を行う.シミュレーションによる評価を行い, 提案解法が従来解法との比較において高速且つ高い配線能力を有していることを示す.

CiNii Books

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

本論文では, 巡回セールスマン問題の遣伝的アルゴリズムに対して凸包による訪問順序制限定理を用いた求解性能向上方法の提案を行う. 凸包とは点集合の任意の2要素を結ぶ閉線分を内部に含む最小の凸多角形であり, 要素数nの点集合の凸包を求めるアルゴリズムの計算量の下界はO (n log n) である. 訪問順序制限定理は凸包を用いてユークリッド平面上の巡回セールスマン問題の最短巡回路となる為の必要条件を示している. また, 遣伝的アルゴリズムは自然界の自然淘汰の過程をモデルとした解法であり, 巡回セールスマン問題の近似解探索アルゴリズムの中でも優れた解法の一つとして知られている. 本論文では, 凸包によって与えられる最短巡回路の必要条件を充足する巡回路を初期染色体とする事により, 遺伝的アルゴリズムの求解性能の向上を図る. ベンチマーク問題を用いたシミュレーションによる性能評価の結果, 都市数の大きい問題において訪問順序制限定理を用いた解法の求解精度が向上した.

CiNii Books

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記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

マキシマムニューロンモデルでは，制約条件の充足のため，互いに素に分割されたニューロングループの中で，最大ポテンシャル（ニューロン入力）を持つ唯一のニューロンを発火状態とする"winner?take?all"方式を採用している．我々は，Takefujiらによって提案されたマキシマムニューロンモデルが，制約条件充足型の組合せ最適化問題に対して，非常に有効なニューラルネットワーク解法を実現することを，N?Queen問題を通して明らかにしてきた．本論文では，複数のニューロンが同時に同一の最大ポテンシャルを有する場合の，競合解消方式に関する提案を行う．N＝500までのN?Queen問題に対するシミュレーションにより，ニューラルネットワークの3種類の状態更新方法（逐次，準同期，同期）における各方式の求解性能を評価し，"previous selection"方式が優れていることを示す．また，提案する競合解消方式を用いた場合の準同期式マキシマムニューロンモデルが，大規模ニューラルネットワークのハードウェア実装に非常に適していることを示す．The maximum neuron model provides efficient neural network solutions for combinatorial optimization problems.In this"winner-take-all"model,one and only one neuron with the maximum input value is always fired in each group of neurons to satisfy the selection constraint.The maximum neuron model can not only limit the searching space,but also reduce the computation load.In this paper,we propose two methods for selecting one neuron among two or more neurons which have the same maximum input value,named"least index method"and"previous selection method".The simulation results in N-queens problems show that the latter method performs better than the former method generally,and that the previous selection method on the N-parallel mode provides the suitable algorithm for hardware implementation of large-scale neural networks.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00013273/

記述言語：日本語   出版者・発行元：一般社団法人情報処理学会  

マキシマムニューロンモデルでは，制約条件の充足のため，互いに素に分割されたニューロングループの中で，最大ポテンシャル（ニューロン入力）を持つ唯一のニューロンを発火状態とする"winner?take?all"方式を採用している．我々は，Takefujiらによって提案されたマキシマムニューロンモデルが，制約条件充足型の組合せ最適化問題に対して，非常に有効なニューラルネットワーク解法を実現することを，N?Queen問題を通して明らかにしてきた．本論文では，複数のニューロンが同時に同一の最大ポテンシャルを有する場合の，競合解消方式に関する提案を行う．N＝500までのN?Queen問題に対するシミュレーションにより，ニューラルネットワークの3種類の状態更新方法（逐次，準同期，同期）における各方式の求解性能を評価し，"previous selection"方式が優れていることを示す．また，提案する競合解消方式を用いた場合の準同期式マキシマムニューロンモデルが，大規模ニューラルネットワークのハードウェア実装に非常に適していることを示す．The maximum neuron model provides efficient neural network solutions for combinatorial optimization problems.In this"winner-take-all"model,one and only one neuron with the maximum input value is always fired in each group of neurons to satisfy the selection constraint.The maximum neuron model can not only limit the searching space,but also reduce the computation load.In this paper,we propose two methods for selecting one neuron among two or more neurons which have the same maximum input value,named"least index method"and"previous selection method".The simulation results in N-queens problems show that the latter method performs better than the former method generally,and that the previous selection method on the N-parallel mode provides the suitable algorithm for hardware implementation of large-scale neural networks.

CiNii Books

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その他リンク： http://id.nii.ac.jp/1001/00013273/

記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

マキシマムニューロンモデルでは, 制約条件の充足のため, 互いに素に分割されたニユーロングループの中で, 最大ポテンシャル(ニューロン入力)をもつ唯一のニューロンのみ発火状態とする"Winner-take-all"方式を採用している. 我々は, Takefujiらによって提案されたマキシマムニューロンモデルが, 制約条件充足型の組合せ最適化問題に対して, 非常に有効なニューラルネットワーク解法を実現することを, N-Queen問題を通して明らかにしている. 本論文では, 複数のニューロンが同時に同ーポテンシャルを有する場合の, 競合解消方式に関する提案を行なう. N=500までのN-Queen問題に対するシミュレーションにより, ニューラルネットワークの3種類の状態更新方法(逐次, 準同期, 同期)における各"Winner-take-all"方式の求解性能を評価する.

CiNii Books

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開催年月日： 2018年11月

開催地：軽井沢  

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開催年月日： 2018年11月

開催地：Phoenix, AZ, USA  

In high-dimensional regression, where the number of explanatory variables is much larger than the sample size, a statistical problem known as the ‘curse of dimensionality’ arises. The strategy of performing all possible regressions is computationally-impractical. We model using non-convex discrete optimization to minimize MSE. We describe an application involving a large number of SNPs in genomic studies for cancer detection.

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開催年月日： 2018年11月

開催地：Phoenix, USA  

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開催地：札幌  

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開催地：大阪大学  

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開催地：Splendor Hotel, Taichung, Taiwan  

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開催地：Swan and Dolphin Hotel, Orlando, Florida, USA  

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開催地：Swan and Dolphin Hotel, Orlando, Florida, USA  

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開催地：大阪大学銀杏会館, 大阪  

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開催地：大阪大学銀杏会館, 大阪  

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Short Papers, 3225

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Short Papers, 3055

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開催地：Osaka, Japan  

Observable characteristics result from two essential factors: genetics and environment. The genomic DNA sequence remains unchanged throughout life, but methyl groups are added or removed depending on these factors. Thus, the methylated genome alters the activity of the genes. We proposed an index to quantify the effects of genetic and environmental factors on DNA methylation using identical twins. The index reveals that environmental factors have a more substantial impact on varying methylation levels. We verified the relationship between disorders and the methylation sites under the strong influence of genetic factors selected by our measurement. The associations between genes and disorders were from MalaCards. The seven of ten genes under the strongest influence on genetic factors were associated with known disorders, which was statistically significant. The result indicates that methylation sites susceptible to genetic factors were significantly associated with disorders. We expect our measurement to quantify genetic factors’ effect on DNA methylation sites to help identify causative genes for various diseases.

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開催地：online  

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開催地：オンライン  

我々が用いている言葉を計算機で解析する事を自然言語解析と呼ぶ。我々が日常的に使用しているグーグル検索も自然言語解析の一例である。本日は明治民法を解析対象とした研究を紹介する。現行民法は1896年(明治29年)に制定され、その後多数の改正を経てきた。明治民法とは、制定当時の民法の事である。 穂積陳重・富井政章・梅謙次郎の3名が起草した明治民法は、不平等条約の解消を一目的としており、フランスをはじめとする西洋各国の法令を参考にしたと言われている。この事柄を、計算機により明治民法の各条と西洋各国民法の各条を網羅的かつ客観的に解析する事で、再確認するとともに、新たな知見を得る切掛として有効である事を示す。 自然言語解析の対象は明治民法に留まるわけではない。 1) 全都道府県の全例規比較、2) 大阪府議会議事録の計算機解析 3) 最高裁判例（刑事）の参照法解析と判事評価 についても、時間の許す範囲で紹介する。

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開催地：オンライン  

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開催地：オンライン  

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開催地：オンライン  

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開催地：オンライン  

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開催地：オンライン  

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開催地：愛知県名古屋市  

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