Updated on 2025/02/06

Information

 

写真a

 
MARUYAMA OSAMU
 
Organization
Faculty of Design Department of Design Futures Professor
School of Design Department of Design（Concurrent）
Graduate School of Design Department of Design（Concurrent）
Title
Professor
Profile
Research activities: It is important to elucidate various the structures, mechanisms and regulations of organisms, which are designed according to the blueprints called 'genomes', in order to understand them as complex systems. I’m tackling this problem by designing and analyzing algorithms to discover biological knowledge and rules from existing data. I’m also carrying out computational experiments on various biological data with the implemented algorithms. Educational activities: An educational purpose is to train computer scientists who can understand biology. Social activities: A manager of Japanese Society for Bioinformatics
Homepage
External link

Degree

  • Dr. Sci.

Research History

  • 1996年4月〜 東京大学医科学研究所ヒトゲノム解析センター助手 2000年5月～ 九州大学大学院数理学研究院助教授 2007年4月～ 九州大学大学院数理学研究院准教授 2011年4月～ 九州大学マス・フォア・インダストリ研究所准教授 2018年4月～ 九州大学大学院芸術工学研究院   

Research Interests・Research Keywords

  • Research theme： Computational Biology

    Keyword： Sequence motif prediction, DNA methylation status prediction, algorithm, machine learning

    Research period： 1996.4

Awards

  • （財）電気通信普及財団 第９回 テレコムシステム技術学生賞佳作

    1994.3   （財）電気通信普及財団  

Papers

  • Preservation of emotional context in tweet embeddings on social networking sites Reviewed

    Osamu Maruyama, Asato Yoshinaga, Ken‑ichi Sawai

    Artifcial Life and Robotics   2024.10

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： https://doi.org/10.1007/s10015-024-00974-3

    Repository Public URL： https://hdl.handle.net/2324/7333693

  • CBOEP: Generating negative enhancer-promoter interactions to train classifiers Reviewed International journal

    Koga, T; Maruyama, O

    14TH ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, BCB 2023   2023   （ ISBN:979-8-4007-0126-9 ）

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics  

    For training and testing enhancer-promoter interaction (EPI) classifiers, the question on which non-positive EPIs are selected as negative instances must be answered. Most previous methods use the dataset of the EPI classifier TargetFinder where negative EP pairs are sampled from non-positive EP pairs. Consequently, over 92% of EPIs in the TargetFinder-positive and negative sets of cell line GM12878 have a 2-fold or greater positive/negative class imbalance of promoter occurrences between the positive and negative EP pairs. This situation negatively impacts the predictability of EPI classifiers trained using the datasets.Thus, we first proposed the condition that the negative EPIs should satisfy. Second, we devised a method called CBOEP (class balanced occurrences of enhancers and promoters), to generate negative EPI sets that approximately fulfil this condition for a given positive EPI set. CBOEP solves the finding problem by reducing it to the maximum-flow problem. Third, we applied the generated negative EPI sets to existing EPI classifiers, TransEPI and TargetFinder. The negative datasets lead to higher prediction performance than the existing negative EPI datasets. The source code is available at https://github.com/maruyama-lab-design/CBOEP.

    DOI： 10.1145/3584371.3612997

    Web of Science

    Scopus

  • CMIC: predicting DNA methylation inheritance of CpG islands with embedding vectors of variable-length k-mers Reviewed International journal

    @Osamu Maruyama #Yinuo Li #Hiroki Narita @Hidehiro Toh @Wan Kin Au Yeung @Hiroyuki Sasaki

    BMC Bioinformatics   23 ( 1 )   371   2022.9   （ ISSN:1471-2105 ）

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：BMC Bioinformatics  

    Background: Epigenetic modifcations established in mammalian gametes are largely
    reprogrammed during early development, however, are partly inherited by the embryo
    to support its development. In this study, we examine CpG island (CGI) sequences to
    predict whether a mouse blastocyst CGI inherits oocyte-derived DNA methylation
    from the maternal genome. Recurrent neural networks (RNNs), including that based on
    gated recurrent units (GRUs), have recently been employed for variable-length inputs
    in classifcation and regression analyses. One advantage of this strategy is the ability
    of RNNs to automatically learn latent features embedded in inputs by learning their
    model parameters. However, the available CGI dataset applied for the prediction of
    oocyte-derived DNA methylation inheritance are not large enough to train the neural
    networks.
    Results: We propose a GRU-based model called CMIC (CGI Methylation Inheritance
    Classifer) to augment CGI sequence by converting it into variable-length k-mers,
    where the length k is randomly selected from the range kmin to kmax, N times, which
    were then used as neural network input. N was set to 1000 in the default setting. In
    addition, we proposed a new embedding vector generator for k-mers called splitDNA2vec. The randomness of this procedure was higher than the previous work,
    dna2vec.
    Conclusions: We found that CMIC can predict the inheritance of oocyte-derived DNA
    methylation at CGIs in the maternal genome of blastocysts with a high F-measure
    (0.93). We also show that the F-measure can be improved by increasing the parameter
    N, that is, the number of sequences of variable-length k-mers derived from a single
    CGI sequence. This implies the efectiveness of augmenting input data by converting a
    DNA sequence to N sequences of variable-length k-mers. This approach can be applied
    to diferent DNA sequence classifcation and regression analyses, particularly those
    involving a small amount of data.

    DOI： 10.1186/s12859-022-04916-3

    Web of Science

    Scopus

    PubMed

    Other Link： https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04916-3

  • A convolutional neural network-based regression model to infer the epigenetic crosstalk responsible for CG methylation patterns. Reviewed International journal

    Wan Kin Au Yeung, Osamu Maruyama, Hiroyuki Sasaki

    BMC Bioinform.   22   341 - 341   2021.6

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1186/s12859-021-04272-8

    Other Link： https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04272-8

  • DegSampler3: Pairwise Dependency Model in Degradation Motif Site Prediction of Substrate Protein Sequences Reviewed International journal

    Osamu Maruyama, Fumiko Matsuzaki

    Proc. of 19th IEEE International Conference on Bioinformatics and Bioengineering   2019.10

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • DegSampler: Collapsed Gibbs sampler for detecting E3 binding sites Reviewed International journal

    @Osamu Maruyama,@Fumiko Matsuzaki

    18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018 Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018   1 - 9   2018.12

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    Language：English   Publishing type：Research paper (international conference proceedings)  

    In this paper, we address the problem of finding sequence motifs in substrate proteins specific to E3 ubiquitin ligases (E3s). We formulated a posterior probability distribution of sites by designing a likelihood function based on amino acid indexing and a prior distribution based on the disorderness of protein sequences. These designs are derived from known characteristics of E3 binding sites in substrate proteins. Then, we devise a collapsed Gibbs sampling algorithm for the posterior probability distribution called DegSampler. We performed computational experiments using 36 sets of substrate proteins specific to E3s and compared the performance of DegSampler with those of popular motif finders, MEME and GLAM2. The results showed that DegSampler was superior to the others in finding E3 binding motifs. Thus, DegSampler is a promising tool for finding E3 motifs in substrate proteins.

    DOI： 10.1109/BIBE.2018.00009

  • RocSampler: Regularizing Overlapping Protein Complexes in Protein-Protein Interaction Networks Reviewed International journal

    Osamu Maruyama, Yuki Kuwahara

    BMC Bioinformatics   18   51 - 62   2017.12

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    Language：English   Publishing type：Research paper (scientific journal)  

    BackgroundIn recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously.ResultsIn this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, http://imi.kyushu-u.ac.jp/~om/software/RocSampler/.ConclusionsWe have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction.

    DOI： 10.1186/s12859-017-1920-5

    Other Link： https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1920-5

  • Regularizing predicted complexes by mutually exclusive protein-protein interactions Reviewed International journal

    Osamu Maruyama, Limsoon Wong

    Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015   1068 - 1075   2015.8

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    Language：English   Publishing type：Research paper (international conference proceedings)  

    Protein complexes are key entities in the cell respon-
    sible for various cellular mechanisms and biological processes. We
    propose here a method for predicting protein complexes from
    a protein-protein interaction (PPI) network, using information
    on mutually exclusive PPIs. If two interactions are mutually
    exclusive, they are not allowed to exist simultaneously in the
    same predicted complex. We introduce a new regularization term
    which checks whether predicted complexes are connected by mu-
    tually exclusive PPIs. This regularization term is added into the
    scoring function of our earlier protein complex prediction tool,
    PPSampler2. We show that PPSampler2 with mutually exclusive
    PPIs outperforms the original one. Furthermore, the performance
    is superior to well-known representative conventional protein
    complex prediction methods. Thus, it is is effective to use mutual
    exclusiveness of PPIs in protein complex prediction.

  • ReSAPP: Predicting overlapping protein complexes by merging multiple-sampled partitions of proteins Reviewed International journal

    So Kobiki, Osamu Maruyama

    Journal of bioinformatics and computational biology   12 ( 6 )   2014.12

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    Language：English   Publishing type：Research paper (scientific journal)  

  • Discovery of small protein complexes from PPI networks with size-specific supervised weighting Reviewed International journal

    Chern Han Yong, Osamu Maruyama, Limsoon Wong

    BMC systems biology 8, S3-S3, 2014.   2014.12

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    Language：English   Publishing type：Research paper (scientific journal)  

  • A scale-free structure prior for Bayesian inference of Gaussian graphical models Reviewed International journal

    Osamu Maruyama, Shota Shikita

    IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014.   2014.11

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • PPSampler2: Predicting Protein Complexes More Accurately and Efficiently by Sampling Reviewed International journal

    Chasanah Kusumastuti Widita, Osamu Maruyama

    BMC Systems Biology   7 ( Suppl 6 )   S14   2013.12

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    Language：English   Publishing type：Research paper (scientific journal)  

    The problem of predicting sets of components of heteromeric protein complexes is a challenging problem in
    Systems Biology. There have been many tools proposed to predict those complexes. Among them, PPSampler, a
    protein complex prediction algorithm based on the Metropolis-Hastings algorithm, is reported to outperform other
    tools. In this work, we improve PPSampler by refining scoring functions and a proposal distribution used inside the
    algorithm so that predicted clusters are more accurate as well as the resulting algorithm runs faster. The new
    version is called PPSampler2. In computational experiments, PPSampler2 is shown to outperform other tools
    including PPSampler. The F-measure score of PPSampler2 is 0.67, which is at least 26% higher than those of the
    other tools. In addition, about 82% of the predicted clusters that are unmatched with any known complexes are
    statistically significant on the biological process aspect of Gene Ontology. Furthermore, the running time is
    reduced to twenty minutes, which is 1/24 of that of PPSampler.

  • Sampling Strategy for Protein Complex Prediction Using Cluster Size Frequency Reviewed International journal

    Daisuke Tatsuke, Osamu Maruyama

    Gene   2012.12

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    Language：English   Publishing type：Research paper (scientific journal)  

    In this paper we propose a Markov chain Monte Carlo sampling method for
    predicting protein complexes from protein-protein interactions (PPIs). Many
    of the existing tools for this problem are designed more or less based on a
    density measure of a subgraph of the PPI network. This kind of measures
    is less effective for smaller complexes. On the other hand, it can be found
    that the number of complexes of a size in a database of protein complexes
    follows a power-law. Thus, most of the complexes are small-sized. For example,
    in CYC2008, a database of curated protein complexes of yeast, 42% of
    the complexes are heterodimeric, i.e., a complex consisting of two different
    proteins. In this work, we propose a protein complex prediction algorithm,
    called PPSampler (Proteins’ Partition Sampler), which is designed based on
    the Metropolis-Hastings algorithm using a parameter representing a target
    value of the relative frequency of the number of predicted protein complexes
    of a particular size. In a performance comparison, PPSampler outperforms
    other existing algorithms. Furthermore, about half of the predicted clusters
    that are not matched with any known complexes in CYC2008 are statistically
    significant by Gene Ontology terms. Some of them can be expected to
    be true complexes.

  • Heterodimeric Protein Complex Identification Reviewed International journal

    Osamu Maruyama

    ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011   2011.8

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • NWE: Node-Weighted Expansion for Protein Complex Prediction Using Random Walk Distances Reviewed International journal

    Osamu Maruyama and Ayaka Chihara

    Proc. IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2010)   2010.12

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • X chromosome-wide analyses of genomic DNA methylation states and gene expression in male and female neutrophils International journal

    Yukio Yasukochi, Osamu Maruyama, Milind C. Mahajan, Carolyn Pad- den, Ghia M. Euskirchen, Vincent Schulz, Hideki Hirakawa, Satoru Kuhara, Xing-Hua Pan, Peter E. Newburger, Michael Snyder, and Sherman M. Weiss- man

    Proceedings of the National Academy of Sciences of the United States of America (PNAS)   107   2010.2

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    Language：English   Publishing type：Research paper (scientific journal)  

  • Evaluating Protein Sequence Signatures Inferred from Protein-Protein Interaction Data by Gene Ontology Annotations Reviewed International journal

    Osamu Maruyama, Hideki Hirakawa, Takao Iwayanagi, Yoshiko Ishida, Shizu Takeda, Jun Otomo, Satoru Kuhara

    2008 IEEE International Conference on Bioinformatics and Biomedicine   2008.11

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • Reconstructing phylogenetic trees of prokaryote genomes by randomly sampling oligopeptides Reviewed International journal

    Osamu Maruyama, Akiko Matsuda, and Satoru Kuhara

    International Journal of Bioinformatics Research and Applicaions (IJBRA) 1(4), 429-446, 2005. (preliminary version has appeared in the Proceedings of the 5th International Conference on Computational Science (ICCS 2005), Lecture Notes in Computer Science 3514-6, Springer-Verlag, II-911-918, 2005).   2005.11

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    Language：English   Publishing type：Research paper (scientific journal)  

  • Searching for Regulatory Elements of Alternative Splicing Events Using Phylogenetic Footprinting, International journal

    Daichi Shigemizu and Osamu Maruyama.

    Proceedings of the 4th Workshop on Algorithms in Bioinformatics, Lecture Notes in Bioinformatics 3240, Springer-Verlag   3240   147 - 158   2004.9

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • Extensive Search for Discriminative Features of Alternative Splicing Reviewed International journal

    Osamu Maruyama

    Pacific Symposium on Biocomputing 2004   54 - 65   2004.1

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • Finding optimal degenerate patterns in DNA sequences Reviewed International journal

    Osamu Maruyama

    Bioinformatics   19   II206 - II214   2003.9

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1093/bioinformatics/btg1079

  • How much emotional information the distributed representations of tweets in SNS preserve Reviewed

    O. Maruyama, A. Yoshinaga, K. Sawai

    Proceedings of the Joint Symposium of The Twenty-Ninth International Symposium on Artificial Life and Robotics (AROB 29th 2024) and The Ninth International Symposium on BioComplexity (ISBC 9th 2024) and The Seventh International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 7th 2024)   460 - 465   2024.1   （ ISSN:2185-3797 ISBN:978-4-9913442-0-6 ）

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

  • Determining the minimum number of protein-protein interactions required to support known protein complexes Reviewed

    Natsu Nakajima, Morihiro Hayashida, Jesper Jansson, Osamu Maruyama, Tatsuya Akutsu

    PLoS One   13 ( 4 )   2018.4

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    Language：English   Publishing type：Research paper (scientific journal)  

    The prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND), and that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85), do not include enough PPIs to form all CYC2008 protein complexes. We also demonstrate that the proposed problem framework and our solutions can enhance the prediction accuracy of existing PPI prediction methods. ILPMinPPI can be freely downloaded from http://sunflower.kuicr.kyoto-u.ac.jp/~nakajima/.

    DOI： 10.1371/journal.pone.0195545

  • Discovery of Tree Structured Patterns Using Markov Chain Monte Carlo Method Reviewed International journal

    Yasuhiro Okamoto, Kensuke Koyanagi, Takayoshi Shoudai, Osamu Maruyama

    Proc. 7th IADIS International Conference on Information Systems 2014, 28th February - 2nd March 2014, Madrid, Spain.   95 - 102   2014.2

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels Reviewed International journal

    Peiying Ruan, Morihiro Hayashida, Osamu Maruyama, Tatsuya Akutsu

    BMC Bioinformatics (APBC 2014)   15(Suppl 2)   S6   2014.1

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： doi:10.1186/1471-2105-15-S2-S6

    Other Link： http://www.biomedcentral.com/1471-2105/15/S2/S6

  • The Purity Measure for Genomic Regions Leads to Horizontally Transferred Genes Reviewed International journal

    Yuta Taniguchi, Yasuhiro Yamada, Osamu Maruyama, Satoru Kuhara, Daisuke Ikeda

    Journal of Bioinformatics and Computational Biology (JBCB)   11 ( 6 )   1343002   2013.12

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    Language：English   Publishing type：Research paper (scientific journal)  

  • Heterodimeric protein complex identification by naïve Bayes classifiers Reviewed International journal

    Osamu Maruyama

    BMC Bioinformatics   14   347   2013.12

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： doi:10.1186/1471-2105-14-347

    Other Link： http://www.biomedcentral.com/1471-2105/14/347

  • Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions Reviewed International journal

    Peiying Ruan, Morihiro Hayashida, Osamu Maruyama, Tatsuya Akutsu

    PLoS ONE   8 ( 6 )   2013.6

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    Language：English   Publishing type：Research paper (scientific journal)  

    Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

    DOI： 10.1371/journal.pone.0065265

    Other Link： http://dx.doi.org/10.1371/journal.pone.0065265

  • Infrequent, Unexpected, and Contrast Pattern Discovery from Bacterial Genomes by Genome-wide Comparative Analysis Reviewed International journal

    Daisuke Ikeda, Osamu Maruyama, Satoru Kuhara

    Proc. of 4th International Conference on Bioinformatics Models, Methods and Algorithms   308 - 311   2013.2

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    Language：English   Publishing type：Research paper (international conference proceedings)  

  • Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model International journal

    Tatsuya Akutsu, Satoru Kuhara, Osamu Maruyama, and Satoru Miyano.

    Theoretical Computer Science   298 ( 1 )   235 - 251   2003.1

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    Language：English  

    DOI： 10.1016/S0304-3975(02)00425-5

  • Fast algorithm for extracting multiple unordered short motifs using bit operations International journal

    O. Maruyama, H. Bannai, Y. Tamada, S.Kuhara, and S.Miyano

    Information Sciences   146 ( 1-4 )   115 - 126   2002.1

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    Language：English  

    DOI： 10.1016/S0020-0255(02)00219-0

  • Extensive feature detection of N-terminal protein sorting signals International journal

    H. Bannai, Y. Tamada, O. Maruyama, K. Nakai, and　S. Miyano

    Bioinformatics   18 ( 2 )   298 - 305   2002.1

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    Language：English  

    DOI： 10.1093/bioinformatics/18.2.298

  • Markov Chain Monte Carlo Algorithms International journal

    Osamu Maruyama

    A Mathematical Approach to Research Problems of Science and Technology, 349-363, 2014.   1900

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    Language：English  

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Books

  • バイオインフォマティクス—配列データ解析と構造予測 (シリーズ予測と発見の科学 4)

    丸山　修，阿久津　達也（Role：Joint author）

    朝倉書店  2007.5 

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    Language：Japanese   Book type：Scholarly book

  • 医学統計学の事典

    丹後　俊郎，小西　貞則　編集（Role：Joint author）

    朝倉書店  2010.6 

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    Responsible for pages：分担執筆，隠れMarkovを用いた配列データ解析，138-141   Language：Japanese   Book type：Scholarly book

    Repository Public URL： http://hdl.handle.net/2324/1001463712

  • コンピュータ支援による科学的知識の発見， 森下真一，宮野悟編，発見科学とデータマイニング

    宮野 悟，丸山 修（Role：Joint translator）

    共立出版  2000.1 

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    Responsible for pages：bit別冊，pp.130--140   Language：Japanese   Book type：Scholarly book

    Pat Langley, The computer-aided discovery of scientific knowledge, Proceedings of the 1st International Conference of Discovery Science ({\\it Lecture Notes in Artificial Intelligence} {\\bf 1532}), Springer-Verlag, 25--39, 1998.)

Presentations

  • How much emotional information the distributed representations of tweets in SNS preserve International conference

    @Osamu Maruyama, #Asato Yoshinaga, @Ken-ichi Sawai

    The Twenty-Ninth International Symposium on Artificial Life and Robotics (AROB 29th 2024)  2024.1 

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    Event date： 2024.1 - 2024.4

    Language：English   Presentation type：Oral presentation (general)  

    Venue：B-Con PLAZA, Beppu, JAPAN and ONLINE   Country：Japan  

    In our communication, emotional information is an important factor. We have considered three different schemes
    to generate embedding vectors of tweets in social media. The embedding generators are based on word2vec, pre-trained BERT
    model, and fine-tuned BERT model. We found that these embedding vectors preserve the emotional information in different
    degrees.

  • DegSampler3: Pairwise Dependency Model in Degradation Motif Site Prediction of Substrate Protein Sequences International conference

    Osamu Maruyama, Fumiko Matsuzaki

    2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019  2019.10 

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    Event date： 2019.10

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Athens   Country：Greece  

    Other Link： https://bibe2019.ics.forth.gr/

  • DegSampler: Collapsed Gibbs Sampler for Detecting E3 Binding Sites International conference

    Osamu Maruyama, Fumiko Matsuzaki

    2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)  2018.12 

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    Event date： 2018.10

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Taichung   Country：Taiwan, Province of China  

    In this paper, we address the problem of finding sequence motifs in substrate proteins specific to E3 ubiquitin ligases (E3s). We formulated a posterior probability distribution of sites by designing a likelihood function based on amino acid indexing and a prior distribution based on the disorderness of protein sequences. These designs are derived from known characteristics of E3 binding sites in substrate proteins. Then, we devise a collapsed Gibbs sampling algorithm for the posterior probability distribution called DegSampler. We performed computational experiments using 36 sets of substrate proteins specific to E3s and compared the performance of DegSampler with those of popular motif finders, MEME and GLAM2. The results showed that DegSampler was superior to the others in finding E3 binding motifs. Thus, DegSampler is a promising tool for finding E3 motifs in substrate proteins.

  • Regularizing predicted complexes by mutually exclusive protein-protein interactions International conference

    Osamu Maruyama, Limsoon Wong

    International Symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics, HI-BI-BI 2015  2015.8 

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    Event date： 2015.8

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Paris   Country：France  

    Protein complexes are key entities in the cell responsible for various cellular mechanisms and biological processes. We
    propose here a method for predicting protein complexes from
    a protein-protein interaction (PPI) network, using information
    on mutually exclusive PPIs. If two interactions are mutually
    exclusive, they are not allowed to exist simultaneously in the
    same predicted complex. We introduce a new regularization term
    which checks whether predicted complexes are connected by mu-
    tually exclusive PPIs. This regularization term is added into the
    scoring function of our earlier protein complex prediction tool,
    PPSampler2. We show that PPSampler2 with mutually exclusive
    PPIs outperforms the original one. Furthermore, the performance
    is superior to well-known representative conventional protein
    complex prediction methods. Thus, it is is effective to use mutual
    exclusiveness of PPIs in protein complex prediction.

  • Sampling Strategy for Protein Complex Prediction Using Cluster Size Frequency International conference

    Tatsuke Daisuke, Osamu Maruyama

    The 23rd International Conference on Genome Informatics  2012.12 

    　More details

    Event date： 2012.12 - 2012.10

    Presentation type：Oral presentation (general)  

    Venue：Tainan, Taiwan   Country：Taiwan, Province of China  

    In this paper we propose a Markov chain Monte Carlo sampling method for
    predicting protein complexes from protein-protein interactions (PPIs). Many
    of the existing tools for this problem are designed more or less based on a
    density measure of a subgraph of the PPI network. This kind of measures
    is less effective for smaller complexes. On the other hand, it can be found
    that the number of complexes of a size in a database of protein complexes
    follows a power-law. Thus, most of the complexes are small-sized. For example,
    in CYC2008, a database of curated protein complexes of yeast, 42% of
    the complexes are heterodimeric, i.e., a complex consisting of two different
    proteins. In this work, we propose a protein complex prediction algorithm,
    called PPSampler (Proteins’ Partition Sampler), which is designed based on
    the Metropolis-Hastings algorithm using a parameter representing a target
    value of the relative frequency of the number of predicted protein complexes
    of a particular size. In a performance comparison, PPSampler outperforms
    other existing algorithms. Furthermore, about half of the predicted clusters
    that are not matched with any known complexes in CYC2008 are statistically
    significant by Gene Ontology terms. Some of them can be expected to
    be true complexes.

    Other Link： http://conf.ncku.edu.tw/giw2012/

  • Evaluating Protein Sequence Signatures Inferred from Protein-Protein Interaction Data by Gene Ontology Annotations International conference

    Osamu Maruyama

    2008 IEEE International Conference on Bioinformatics and Biomedicine  2008.11 

    　More details

    Event date： 2008.11

    Presentation type：Oral presentation (general)  

    Venue：Philadelphia, PA   Country：United States  

    Other Link： http://www.ischool.drexel.edu/ieeebibm/bibm08/about/bibm-about.asp

  • CBOEP: Generating negative enhancer-promoter interactions to train classifiers International conference

    #Tsukasa Koga, Osamu Maruyama

    The 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)  2023.9 

    　More details

    Event date： 2023.9

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Houston, Texas   Country：United States  

    For training and testing enhancer-promoter interaction (EPI) clas-
    sifiers, the question on which non-positive EPIs are selected as
    negative instances must be answered. Most previous methods use
    the dataset of the EPI classifier TargetFinder where negative EP
    pairs are sampled from non-positive EP pairs. Consequently, over
    92% of EPIs in the TargetFinder-positive and negative sets of cell
    line GM12878 have a 2-fold or greater positive/negative class imbal-
    ance of promoter occurrences between the positive and negative
    EP pairs. This situation negatively impacts the predictability of EPI
    classifiers trained using the datasets.
    Thus, we first proposed the condition that the negative EPIs
    should satisfy. Second, we devised a method called CBOEP (class
    balanced occurrences of enhancers and promoters), to generate
    negative EPI sets that approximately fulfil this condition for a given
    positive EPI set. CBOEP solves the finding problem by reducing it to
    the maximum-flow problem. Third, we applied the generated nega-
    tive EPI sets to existing EPI classifiers, TransEPI and TargetFinder.
    The negative datasets lead to higher prediction performance than
    the existing negative EPI datasets. The source code is available at
    https://github.com/maruyama-lab-design/CBOEP.

    Other Link： https://acm-bcb.org/2023/index.php

  • Recurrent neural network approach for predicting DNA methylation inheritance of CpG islands using embedding vectors of variable-length k-mers Invited International conference

    Osamu Maruyama

    The International Symposium "Totipotency and Germ Cell Development"  2022.11 

    　More details

    Event date： 2022.11

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Centennial Hall Kyushu University School of Medicine Maidashi 3-1-1, Higashiku, Fukuoka 812-8582   Country：Japan  

  • 埋め込みベクトルによるCpGアイランドのメチル化状態予測

    #成田 浩規(九州大学), Au Yeung Wan Kin(九州大学), 佐々木 裕之(九州大学), 丸山 修(九州大学)

    第69回バイオ研究発表会  2022.2 

    　More details

    Event date： 2022.3

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • エンハンサー・プロモーター間相互作用予測問題に対する負例生成手法の提案

    #古賀 吏(九州大学), 丸山 修(九州大学)

    第69回バイオ研究発表会  2022.2 

    　More details

    Event date： 2022.3

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • エンハンサー・プロモーター間相互作用の負例生成手法とその評価

    #古賀 吏(九州大学), 丸山 修(九州大学)

    第73回バイオ研究発表会  2023.3 

    　More details

    Event date： 2022.3

    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Predicting Discriminative Motifs for DNA Methylation in Mammalian Development

    #Ryo Shimizu, Wan Kin Au Yeung, Hidehiro Toh, Hiroyuki Sasaki and Osamu Maruyama

    2020日本バイオインフォマティクス学会年会　第9回生命医薬情報学連合大会 IIBMP2020  2020.9 

    　More details

    Event date： 2020.9

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

    Other Link： https://www.jsbi.org/iibmp2020/

  • 正負例配列集合のためのコンセンサス・モチーフによるクラスタリング・アルゴリズム

    丸山　修

    日本バイオインフォマティクス学会（JSBi）  2017.10 

    　More details

    Event date： 2017.10

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：宮崎市民プラザ　4F 学習室   Country：Japan  

  • MCMC Strategy for Protein Complex Prediction Using Cluster Size Frequency

    Daisuke Tatsuke, Osamu Maruyama

    第11回電子情報通信学会情報論的学習理論と機械学習(IBISML)研究会 —第15回情報論的学習理論ワークショップ(IBIS2012)—  2012.11 

    　More details

    Event date： 2012.11

    Venue：筑波大学 東京キャンパス文京校舎   Country：Japan  

    In this paper we propose a Markov chain Monte Carlo sampling method
    for predicting protein complexes from protein-protein interactions (PPIs).
    Many of the existing tools for this problem are designed more or less based on
    a density measure of a subgraph of the PPI network.
    This kind of measures is less effective for smaller complexes.
    On the other hand,
    it can be found that
    the frequency of complexes of size, $i$, in a database of protein complexes
    often follows a power-law,
    $i^{-\gamma}$, where $\gamma$ is a constant.
    Thus, most of the complexes are small-sized.
    For example, in CYC2008, a database of curated protein complexes of yeast,
    42\% of the complexes are heterodimeric, i.e.,
    a complex consisting of two different proteins.
    In this work,
    we propose
    a protein complex prediction algorithm, called {\OurMethodName} ({\OurMethodFullName}),
    which is designed based on the Metropolis-Hastings algorithm
    using a parameter representing a target value of
    the relative frequency of the number of
    predicted protein complexes of a particular size.
    In a performance comparison,
    {\OurMethodName} outperforms other existing algorithms.
    Furthermore,
    about half of the predicted clusters that are not matched with
    any known complexes in CYC2008 are statistically significant by
    Gene Ontology terms.
    Some of them can be expected to be true complexes.

  • Protein complex prediction by sampling Invited

    Osamu Maruyama

    平成24年度文部科学省数学・数理科学と諸科学・産業 との連携研究ワークショップ  2012.11 

    　More details

    Event date： 2012.11

    Presentation type：Oral presentation (general)  

    Venue：JR博多シティ会議室   Country：Japan  

    Protein complexes are important entities to organize various biological processes in
    the cell, like signal transduction, gene expression, and molecular transmission. Many
    proteins are known to perform their intrinsic tasks in association with their specific
    interacting partners, forming protein complexes. Therefore, an enriched catalog of
    protein complexes in a cell could accelerate further research to elucidate the mechanisms
    underlying many biological processes. However, known complexes are still limited.
    Thus, it is a challenging problem to computationally predict protein complexes.
    Many of existing tools are designed more or less based on density measures of a
    subgraph of the protein-protein interaction network. This kind of measures is less
    effective for smaller complexes. On the other hand, it can be found that the frequency
    distribution of the number of complexes of size, i, in a database of protein complexes is
    often scale-free, i.e., follows a power-law, i

  • Protein Complex Prediction Invited International conference

    Osamu Maruyama

    2012.10 

    　More details

    Event date： 2012.10

    Presentation type：Oral presentation (general)  

    Venue：Fukuoka International Congress Center   Country：Japan  

    In this talk, we will consider the problem of protein complex prediction,
    which is a challenging problem in computational biology. After a brief
    introduction of this problem, we will present a few computational models used in
    prediction algorithms, some of which are based on random walks with restarts
    and MCMC (Markov chain Monte Carlo) sampling methods.

  • Protein complex prediction Invited International conference

    Osamu Maruyama

    Joint Workshop of IMS and IMI on Mathematics for Industry: Biological and Climatic Prospects  2012.9 

    　More details

    Event date： 2012.9 - 2012.10

    Presentation type：Oral presentation (general)  

    Venue：Institute for Mathematical Sciences, National University of Singapore   Country：Singapore  

    In this talk, we will consider the problem of protein complex prediction, which is a challenging problem in computational biology. After a brief introduction of this problem, we will present a few computational models used in prediction algorithms, some of which are based on random walks with restarts and MCMC (Markov chain Monte Carlo) sampling methods.

    Other Link： http://www2.ims.nus.edu.sg/Programs/012wind/index.php

  • タンパク質複合体サイズ分布を用いたマルコフ連鎖モンテカルロ法に基づく複合体予測手法の研究

    田附大典，丸山修

    第30回情報処理学会バイオ情報学研究会  2012.8 

    　More details

    Event date： 2012.8

    Presentation type：Oral presentation (general)  

    Venue：九州工業大学情報工学部、飯塚キャンパス　総合研究棟大学院セミナー室Ｎ５１１   Country：Japan  

    本研究では，タンパク質間相互作用情報からタンパク質複合体を予測するサンプリング手法を提案
    する．既存手法の多くはタンパク質間相互作用ネットワークの部分グラフの密度に基づき複合体を予測す
    るので，小さな複合体の正確な予測は相対的に困難である．ところが，酵母の代表的なタンパク質複合体
    データベースであるCYC2008 を調べると，複合体のサイズ分布はスケール・フリーであり，42%の複合
    体は最小サイズ2 であることが分かる．そこで，本研究では，複合体のサイズ分布情報を活用したメトロ
    ポリス-ヘイスティングス法に基づく予測手法PPSampler (Proteins’ Partition Sampler) を提案する．こ
    のPPSampler が，既存手法と比べて高い精度を実現することを計算機実験により確認した．

  • Heterodimeric Protein Complex Identification International conference

    Osamu Maruyama

    ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011  2011.8 

    　More details

    Event date： 2011.8

    Presentation type：Oral presentation (general)  

    Venue：Chicago   Country：United States  

    Other Link： http://acmbcb.org/

  • Toward Drawing an Atlas of Hypothesis Classes: Approximating a Hypothesis via Another Hypothesis Model International conference

    O.Maruyama

    The 5nd International Conference of Discovery Science  2002.11 

    　More details

    Presentation type：Oral presentation (general)  

    Venue：{u}beck   Country：Germany  

  • Finding optimal degenerate patterns in DNA sequences International conference

    Osamu Maruyama

    European Conference on Computational Biology (ECCB 2003)  2003.9 

    　More details

    Presentation type：Oral presentation (general)  

    Venue：パリ   Country：France  

  • 最適degenerate pattern探索アルゴリズムと転写因子結合部位同定問題への適用 Invited

    丸山　修

    情報処理学会第91回アルゴリズム研究会  2003.9 

    　More details

    Presentation type：Oral presentation (general)  

    Venue：広島市立大学   Country：Japan  

  • Searching for Regulatory Elements of Alternative Splicing Events Using　Phylogenetic Footprinting International conference

    O.Maruyama

    The 4th Workshop on Algorithms in Bioinformatics,  2004.9 

    　More details

    Presentation type：Oral presentation (general)  

    Venue：Bergen   Country：Norway  

▼display all

Works

Professional Memberships

  • 日本バイオインフォマティクス学会

  • International Society for Computational Biology

  • Institute of Electronics, Information and Communication Engineers(IEICE)

Academic Activities

  • IEEE International Conference on Bioinformatics and Biomedicine (BIBM) International contribution

    Role(s)： Peer review

    2023.8 - 2023.10

    　More details

    Type：Academic society, research group, etc. 

  • Screening of academic papers

    Role(s)： Peer review

    2022

    　More details

    Type：Peer review 

    Number of peer-reviewed articles in foreign language journals：1

  • Screening of academic papers

    Role(s)： Peer review

    2021

    　More details

    Type：Peer review 

    Number of peer-reviewed articles in foreign language journals：1

  • 2020年日本バイオインフォマティクス学会年会・第９回生命医薬情報学連合大会（IIBMP2020）

    Role(s)： Peer review

    2020.6

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Computational Systems-Biology and Bioinformatics (CSBio) International contribution

    Role(s)： Peer review

    2020.6

    　More details

    Type：Academic society, research group, etc. 

  • 座長 International contribution

    2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019  （ Athens Greece ） 2019.10

    　More details

    Type：Competition, symposium, etc. 

  • IEEE International Conference on Bioinformatics and Biomedicine (BIBM) International contribution

    Role(s)： Peer review

    2019.6

    　More details

    Type：Academic society, research group, etc. 

  • Screening of academic papers

    Role(s)： Peer review

    2019

    　More details

    Type：Peer review 

    Number of peer-reviewed articles in foreign language journals：0

    Number of peer-reviewed articles in Japanese journals：0

    Proceedings of International Conference Number of peer-reviewed papers：6

    Proceedings of domestic conference Number of peer-reviewed papers：0

  • The 9th IEEE International Conference on Awareness Science and Technology (iCAST 2018) International contribution

    2018.1 - 2018.12

    　More details

    Type：Academic society, research group, etc. 

  • Asia Pacific Bioinformatics Conference (APBC) International contribution

    Role(s)： Peer review

    2018.1 - 2018.12

    　More details

    Type：Academic society, research group, etc. 

  • The 9th IEEE International Conference on Awareness Science and Technology (iCAST 2018) International contribution

    Role(s)： Peer review

    2018.1 - 2018.12

    　More details

    Type：Academic society, research group, etc. 

  • Screening of academic papers

    Role(s)： Peer review

    2018

    　More details

    Type：Peer review 

    Proceedings of International Conference Number of peer-reviewed papers：18

  • International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) International contribution

    Role(s)： Peer review

    2017.1 - 2017.12

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Genome Informatics (GIW) International contribution

    Role(s)： Peer review

    2017.1 - 2017.12

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Bioinformatics (InCoB) International contribution

    Role(s)： Peer review

    2017.1 - 2017.12

    　More details

    Type：Academic society, research group, etc. 

  • International Symposium on Network Analysis and Mining for Health Informatics, Biomedicine and Bioinformatics (Net-HI-BI-BI) International contribution

    Role(s)： Peer review

    2016.1 - 2016.12

    　More details

    Type：Academic society, research group, etc. 

  • Workshop on Advances in Artificial Intelligence and Bioinformatics (IJCAI\_BAI) International contribution

    Role(s)： Peer review

    2015.1 - 2017.12

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Computational Systems-Biology and Bioinformatics (CSBio) International contribution

    Role(s)： Peer review

    2015.1 - 2015.12

    　More details

    Type：Academic society, research group, etc. 

  • International Symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics (HI-BI-BI) International contribution

    Role(s)： Peer review

    2013.1 - 2015.12

    　More details

    Type：Academic society, research group, etc. 

  • International Symposium on Network Analysis and Mining for Health Informatics, Biomedicine and Bioinformatics (Net-HI-BI-BI) International contribution

    Role(s)： Peer review

    2013.1 - 2013.12

    　More details

    Type：Academic society, research group, etc. 

  • 座長（Chairmanship） International contribution

    GIW  （ Taiwan ） 2012.12

    　More details

    Type：Competition, symposium, etc. 

  • 座長（Chairmanship）

    第30回情報処理学会バイオ情報学研究会  （ Japan ） 2012.8

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    Type：Competition, symposium, etc. 

  • International Conference on Genome Informatics (GIW) International contribution

    Role(s)： Peer review

    2012.1 - 2014.12

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Genome Informatics (GIW) International contribution

    Role(s)： Peer review

    2012.1

    　More details

    Type：Academic society, research group, etc. 

  • 座長（Chairmanship）

    2010年日本バイオインフォマティクス学会年会  （ Japan ） 2010.12

    　More details

    Type：Competition, symposium, etc. 

  • 組織委員会委員

    2010年日本バイオインフォマティクス学会年会  （ Japan ） 2010.12

    　More details

    Type：Competition, symposium, etc. 

  • IEEE International Conference on Bioinformatics and Biomedicine (BIBM) International contribution

    Role(s)： Peer review

    2007.1 - 2017.12

    　More details

    Type：Academic society, research group, etc. 

  • IEEE International Conference on Bioinformatics and Bioengineering (BIBE) International contribution

    Role(s)： Peer review

    2007.1 - 2007.12

    　More details

    Type：Academic society, research group, etc. 

  • International Symposium on Bioinformatics Research and Applications International contribution

    Role(s)： Peer review

    2007.1

    　More details

    Type：Academic society, research group, etc. 

  • IEEE International Conference on Bioinformaticsand Biomedicine International contribution

    Role(s)： Peer review

    2007.1

    　More details

    Type：Academic society, research group, etc. 

  • International Workshop on Bioinformatics Research and Applications (ISBRA) International contribution

    Role(s)： Peer review

    2006.1 - 2016.12

    　More details

    Type：Academic society, research group, etc. 

  • International Workshop on Bioinformatics Research and Applications International contribution

    Role(s)： Peer review

    2006.1

    　More details

    Type：Academic society, research group, etc. 

  • International Conference on Genome Informatics (GIW) International contribution

    Role(s)： Peer review

    2004.1 - 2006.12

    　More details

    Type：Academic society, research group, etc. 

▼display all

Research Projects

  • Mathematical analysis and applications in the 3D genome

    Grant number：21H03544  2021 - 2024

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    丸山 修

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

    本研究の目的は生物実験で得られるゲノムの断片的立体構造情報を有効活用する情報科学的手法の研究である．近年Hi-C法の実験により部分的であるがゲノムの立体構造情報が得られるが，このデータからどれほどの情報を抽出できるかが急務の課題である．そこで3つの課題に取り組む：
    1. Hi-Cデータから得られるゲノム領域間の近接情報を表すコンタクトマップ(CM)の解像度を高める機械学習手法の開発
    2. Hi-C CMを用いた遠位プロモーターとエンハンサーの相互作用同定問題を解く計算手法の開発
    3. Hi-C CMからゲノムの立体構造を予測する手法の開発．以上により立体構造言語としてのゲノムの数理的解析を実施する．

    CiNii Research

  • Diversity and similarity of neural representation associated with conscious experience of color

    Grant number：19H04198  2019.4 - 2024.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    平松 千尋, 丸山 修, 元村 祐貴

    　 More details

    Authorship：Coinvestigator(s)  Grant type：Scientific research funding

    ヒトの色覚には多様性があり、同じ光波長に対する知覚は受容器の特性により異なる。しかし、遺伝的に色覚が異なる人々の間でも、ある色刺激を同じ色としてカテゴライズする場合があることから、色に関する神経表現の多様性と共通性が予測される。本研究では、異なる色覚を持つ人々が同じ色を見ているときの神経活動パターンから、見ていた色のデコーディングに重要な神経表現の特徴量を抽出する。特徴量の共通性と相違から、他者が直接体験できず、神経科学のハードプロブレムとされている主観的な感覚意識体験が、神経表現のどのような共通性と多様性に立脚しているかを究明する。

    CiNii Research

  • 色の感覚意識体験に関連する神経表現の共通性と多様性

    2019 - 2023

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    　 More details

    Authorship：Coinvestigator(s)  Grant type：Scientific research funding

  • Omics approaches towards the elucidation of the molecular network regulating the developmental capacity of the mammalian oocyte

    Grant number：18H05214  2018.4 - 2023.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Specially Promoted Research

    SASAKI Hiroyuki

    　 More details

    Authorship：Coinvestigator(s)  Grant type：Scientific research funding

    By applying genome editing and small-scale omics approaches, we have revealed the molecular network regulating histone modifications and DNA methylation in mouse oocytes, which are essential for mammalian development. We discovered that several histone modifications impact the efficiency and distribution of DNA methylation and identified the proteins and their domains that mediate the epigenetic crosstalk. Further, we found that, while DNA methylation established in oocytes regulates the expression of the maternally imprinted genes almost exclusively in embryos, histone modifications and their responsive factors have a wider effect and regulate various cellular function including chromosome segregation. Finally, we developed a machine-learning-based model that predicts the heritability of DNA methylation to the next generation based on DNA sequence. The findings provide a basis for the study of infertility, various diseases, and improvement of assisted reproductive technologies.

    CiNii Research

  • 多階層オミックスによる卵子の発生能制御分子ネットワークの解明

    2018 - 2022

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Specially Promoted Research

    　 More details

    Authorship：Coinvestigator(s)  Grant type：Scientific research funding

  • 混合正則化モデリングを軸としたヘテロ生物データ群からの機械学習の研究

    Grant number：17K00407  2017 - 2019

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

  • 大規模バイオデータに対する混合正則化モデリングと最適化サンプリング技法の研究

    Grant number：26330330  2014 - 2016

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

  • モチーフ発見の理論的限界

    2007

    Japan Society for the Promotion of Science  特定国派遣

    　 More details

    Authorship：Principal investigator  Grant type：Joint research

  • ヘテロな検索空間に対する最適パターン探索アルゴリズムの構築とゲノムデータへの適用

    Grant number：16700146  2004 - 2006

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (B)

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

  • 属性の創造と探索によるDNAシグナル配列発見方式の研究

    Grant number：13780290  2001 - 2002

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Encouragement of Scientists (A)

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

  • グラフの局所情報からのグラフを復元するためのグラフ形成規則の定式化と学習方式の研究

    Grant number：09780253  1997 - 1998

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Encouragement of Scientists (A)

    　 More details

    Authorship：Principal investigator  Grant type：Scientific research funding

▼display all

Educational Activities

  • Graduate school:
    Introduction to algorithms (Graduate School of Systems Life Sciences)
    Introduction to mathematical models in bioinformatics (Graduate School
    of Systems Life Sciences)
    Applied mathematics IV (Graduate School of Mathematics)
    Applied mathematics D (Graduate School of Engineering)
    Graduate school(since 2018)：
    情報統計学特論(Advanced computational statistics)
    機械学習特論(Advanced machine learning)
    デザイン人間科学特論Ａ(Advanced Human Science A)
    デザイン人間科学特論B(Advanced Human Science B)

    Undergraduate school:
    theoretical computer science
    statistics
    complex analysis (Faculty of Engineering)
    Bioinformatics(Faculty of Physics)
    Undergraduate school(since 2018):
    芸術情報設計概論
    芸術情報プロジェクト
    芸術情報総合演習
    Statistics and data science (since 2020)
    Machine learning (since 2020)

    Visiting associate professor of Institute for Chemical Research, Kyoto
    University (2005)

    Lecturer of Education and Research Organization for Genome Information
    Science
    (2005)

Class subject

  • 情報科学Ⅱ

    2024.12 - 2025.2   Winter quarter

  • 卒業研究Ⅰ

    2024.10 - 2025.3   Second semester

  • 芸術工学演習（未来共生デザインコース）

    2024.10 - 2025.3   Second semester

  • 芸術工学特別研究Ⅳ（未来共生デザインコース）

    2024.10 - 2025.3   Second semester

  • 芸術工学特別研究Ⅱ（未来共生デザインコース）

    2024.10 - 2025.3   Second semester

  • 卒業研究Ⅱ

    2024.10 - 2025.3   Second semester

  • コース融合プロジェクトB（m. 感情の数理的表現と応用）

    2024.10 - 2024.12   Fall quarter

  • スタジオプロジェクトⅢ-B（感情を深層学習でモデリング）

    2024.10 - 2024.12   Fall quarter

  • スタジオプロジェクトⅢ-A（感情を深層学習でモデリング）

    2024.10 - 2024.12   Fall quarter

  • 情報科学Ⅰ

    2024.10 - 2024.12   Fall quarter

  • 未来構想デザイン概論

    2024.10 - 2024.12   Fall quarter

  • 統計情報学（旧：機械学習特論）

    2024.6 - 2024.8   Summer quarter

  • 統計・データ科学

    2024.4 - 2024.9   First semester

  • 芸術工学特別研究Ⅲ（未来共生デザインコース）

    2024.4 - 2024.9   First semester

  • 芸術工学特別研究Ⅰ（未来共生デザインコース）

    2024.4 - 2024.9   First semester

  • 卒業研究Ⅰ

    2024.4 - 2024.9   First semester

  • 情報科学Ⅲ

    2024.4 - 2024.6   Spring quarter

  • 情報科学Ⅱ

    2023.12 - 2024.2   Winter quarter

  • 卒業研究Ⅱ

    2023.10 - 2024.3   Second semester

  • 卒業研究Ⅱ（2019年度以前入学者対象）

    2023.10 - 2024.3   Second semester

  • 卒業研究Ⅰ（2019年度以前入学者対象）

    2023.10 - 2024.3   Second semester

  • 芸術工学演習（未来共生デザインコース）

    2023.10 - 2024.3   Second semester

  • 芸術工学特別研究Ⅳ（未来共生デザインコース）

    2023.10 - 2024.3   Second semester

  • 芸術工学特別研究Ⅱ（未来共生デザインコース）

    2023.10 - 2024.3   Second semester

  • 情報科学Ⅰ

    2023.10 - 2023.12   Fall quarter

  • コース融合プロジェクトA（d. 実データを用いた機械学習演習）

    2023.6 - 2023.8   Summer quarter

  • コース融合プロジェクトB（d. 実データを用いた機械学習演習）

    2023.6 - 2023.8   Summer quarter

  • 機械学習（システム工学）

    2023.4 - 2024.3   Full year

  • 機械学習

    2023.4 - 2024.3   Full year

  • 卒業研究Ⅰ

    2023.4 - 2023.9   First semester

  • 卒業研究Ⅱ（2019年度以前入学者対象）

    2023.4 - 2023.9   First semester

  • 卒業研究Ⅰ（2019年度以前入学者対象）

    2023.4 - 2023.9   First semester

  • 統計・データ科学

    2023.4 - 2023.9   First semester

  • 統計・データ科学（推測統計学）

    2023.4 - 2023.9   First semester

  • 芸術工学特別研究Ⅲ（未来共生デザインコース）

    2023.4 - 2023.9   First semester

  • 芸術工学特別研究Ⅰ（未来共生デザインコース）

    2023.4 - 2023.9   First semester

  • 統計情報学（旧：機械学習特論）

    2023.4 - 2023.6   Spring quarter

  • 情報科学Ⅱ

    2022.12 - 2023.2   Winter quarter

  • 機械学習

    2022.10 - 2023.3   Second semester

  • 卒業研究Ⅰ

    2022.10 - 2023.3   Second semester

  • 卒業研究Ⅱ

    2022.10 - 2023.3   Second semester

  • プラットフォーム演習 C

    2022.10 - 2023.3   Second semester

  • 機械学習（システム工学）

    2022.10 - 2023.3   Second semester

  • 情報科学Ⅰ

    2022.10 - 2022.12   Fall quarter

  • 統計・データ科学（環境設計学科2019年度以前入学者）

    2022.4 - 2023.3   Full year

  • 卒業研究Ⅰ

    2022.4 - 2022.9   First semester

  • 数学共創概論I（数理モデル概論）

    2022.4 - 2022.9   First semester

  • 統計・データ科学

    2022.4 - 2022.9   First semester

  • 統計情報学（旧：機械学習特論）

    2022.4 - 2022.6   Spring quarter

  • 人間科学とデザイン

    2022.4 - 2022.6   Spring quarter

  • 情報科学Ⅲ

    2022.4 - 2022.6   Spring quarter

  • プラットフォーム演習 C

    2021.12 - 2022.2   Winter quarter

  • 情報科学Ⅱ

    2021.12 - 2022.2   Winter quarter

  • デザイン人間科学特別演習Ⅲ

    2021.10 - 2022.3   Second semester

  • 機械学習

    2021.10 - 2022.3   Second semester

  • 機械学習（システム工学）

    2021.10 - 2022.3   Second semester

  • 芸術情報総合演習

    2021.10 - 2022.3   Second semester

  • 卒業研究Ⅰ

    2021.10 - 2022.3   Second semester

  • 卒業研究Ⅱ

    2021.10 - 2022.3   Second semester

  • デザイン人間科学特論Ｂ

    2021.10 - 2022.3   Second semester

  • デザイン人間科学特別演習Ⅰ

    2021.10 - 2022.3   Second semester

  • デザイン人間科学特別演習Ⅱ

    2021.10 - 2022.3   Second semester

  • Advanced Human Science B

    2021.10 - 2022.3   Second semester

  • 情報科学Ⅰ

    2021.10 - 2021.12   Fall quarter

  • 未来構想デザイン概論

    2021.10 - 2021.12   Fall quarter

  • Advanced Machine Learning

    2021.6 - 2021.8   Summer quarter

  • 機械学習特論

    2021.6 - 2021.8   Summer quarter

  • インターンシップ（学部）

    2021.4 - 2022.3   Full year

  • インターンシップ（学部）Ⅰ

    2021.4 - 2022.3   Full year

  • インターンシップ（学部）Ⅱ

    2021.4 - 2022.3   Full year

  • 数学共創概論Ⅰ

    2021.4 - 2021.9   First semester

  • 数学共創概論I（数理モデル概論）

    2021.4 - 2021.9   First semester

  • 統計・データ科学

    2021.4 - 2021.9   First semester

  • 統計・データ科学（推測統計学）

    2021.4 - 2021.9   First semester

  • 芸術情報プロジェクト演習

    2021.4 - 2021.9   First semester

  • 卒業研究Ⅰ

    2021.4 - 2021.9   First semester

  • 卒業研究Ⅱ

    2021.4 - 2021.9   First semester

  • デザイン人間科学特論Ａ

    2021.4 - 2021.9   First semester

  • デザイン人間科学特別演習Ⅰ

    2021.4 - 2021.9   First semester

  • デザイン人間科学特別演習Ⅱ

    2021.4 - 2021.9   First semester

  • Advanced Human Science A

    2021.4 - 2021.9   First semester

  • デザイン人間科学特別演習Ⅲ

    2021.4 - 2021.9   First semester

  • Advanced Computational Statistics

    2021.4 - 2021.6   Spring quarter

  • 人間科学とデザイン

    2021.4 - 2021.6   Spring quarter

  • 情報統計学特論

    2021.4 - 2021.6   Spring quarter

  • Advanced Human Science B

    2020.10 - 2021.3   Second semester

  • 機械学習

    2020.10 - 2021.3   Second semester

  • 機械学習（画像・芸情H29年度以前入学者）

    2020.10 - 2021.3   Second semester

  • 芸術情報総合演習

    2020.10 - 2021.3   Second semester

  • 卒業研究Ⅰ

    2020.10 - 2021.3   Second semester

  • 卒業研究Ⅱ

    2020.10 - 2021.3   Second semester

  • デザイン人間科学特論Ｂ

    2020.10 - 2021.3   Second semester

  • デザイン人間科学特別演習Ⅰ

    2020.10 - 2021.3   Second semester

  • デザイン人間科学特別演習Ⅱ

    2020.10 - 2021.3   Second semester

  • 未来構想デザイン概論

    2020.10 - 2020.12   Fall quarter

  • Advanced machine learning

    2020.6 - 2020.8   Summer quarter

  • 機械学習特論

    2020.6 - 2020.8   Summer quarter

  • Advanced Human Science A

    2020.4 - 2020.9   First semester

  • 統計・データ科学

    2020.4 - 2020.9   First semester

  • 統計・データ科学（推測統計学）

    2020.4 - 2020.9   First semester

  • 芸術情報プロジェクト演習

    2020.4 - 2020.9   First semester

  • 卒業研究Ⅰ

    2020.4 - 2020.9   First semester

  • 卒業研究Ⅱ

    2020.4 - 2020.9   First semester

  • デザイン人間科学特論Ａ

    2020.4 - 2020.9   First semester

  • デザイン人間科学特別演習Ⅰ

    2020.4 - 2020.9   First semester

  • デザイン人間科学特別演習Ⅱ

    2020.4 - 2020.9   First semester

  • デザイン人間科学特別演習Ⅲ

    2020.4 - 2020.9   First semester

  • 統計データ解析特論

    2020.4 - 2020.9   First semester

  • Advanced computational statistics

    2020.4 - 2020.6   Spring quarter

  • 情報統計学特論

    2020.4 - 2020.6   Spring quarter

  • Advanced Human Science B

    2019.10 - 2020.3   Second semester

  • デザイン人間科学特論Ｂ

    2019.10 - 2020.3   Second semester

  • Advanced machine learning

    2019.6 - 2019.8   Summer quarter

  • 機械学習特論

    2019.6 - 2019.8   Summer quarter

  • 芸術情報設計概論

    2019.4 - 2019.9   First semester

  • 芸術情報設計総合演習

    2019.4 - 2019.9   First semester

  • 芸術情報設計プロジェクト演習

    2019.4 - 2019.9   First semester

  • 未来構想デザイン演習

    2019.4 - 2019.9   First semester

  • デザイン人間科学特論Ａ

    2019.4 - 2019.9   First semester

  • 統計データ解析特論

    2019.4 - 2019.9   First semester

  • Advanced Human Science A

    2019.4 - 2019.9   First semester

  • Advanced computational statistics

    2019.4 - 2019.6   Spring quarter

  • 情報統計学特論

    2019.4 - 2019.6   Spring quarter

  • Advanced Human Science B

    2018.10 - 2019.3   Second semester

  • デザイン人間科学特論Ｂ

    2018.10 - 2019.3   Second semester

  • Advanced machine learning

    2018.6 - 2018.8   Summer quarter

  • 機械学習特論

    2018.6 - 2018.8   Summer quarter

  • 芸術情報設計総合演習

    2018.4 - 2018.9   First semester

  • 芸術情報設計プロジェクト演習

    2018.4 - 2018.9   First semester

  • 芸術情報設計概論

    2018.4 - 2018.9   First semester

  • 芸術情報設計概論

    2018.4 - 2018.9   First semester

  • デザイン人間科学特論Ａ

    2018.4 - 2018.9   First semester

  • 統計データ解析特論

    2018.4 - 2018.9   First semester

  • Ａｄｖａｎｃｅｄ　Ｈｕｍａｎ　Ｓｃｉｅｎｃｅ　Ａ

    2018.4 - 2018.9   First semester

  • Statistical Data Analysis

    2018.4 - 2018.9   First semester

  • Advanced computational statistics

    2018.4 - 2018.6   Spring quarter

  • 情報統計学特論

    2018.4 - 2018.6   Spring quarter

  • MMA講究D

    2017.10 - 2018.3   Second semester

  • 応用数学Ｄ

    2017.10 - 2018.3   Second semester

  • 生命情報数理モデル特論

    2017.10 - 2017.12   Fall quarter

  • 応用数学D

    2017.10 - 2017.12   Fall quarter

  • 応用数学Ⅳ

    2017.10 - 2017.12   Fall quarter

  • 生命情報数理モデル特論

    2017.10 - 2017.12   Fall quarter

  • 生命情報数理モデル特論

    2017.10 - 2017.12   Fall quarter

  • ﾌﾟﾛｸﾞﾗﾐﾝｸﾞ演習

    2017.4 - 2017.9   First semester

  • 応用数学Ⅰ

    2017.4 - 2017.6   Spring quarter

  • 応用数学Ａ

    2017.4 - 2017.6   Spring quarter

  • 応用数学Ａ

    2017.4 - 2017.6   Spring quarter

  • 情報数学特論１

    2015.10 - 2016.3   Second semester

  • 応用数学Ⅳ(D)

    2015.10 - 2016.3   Second semester

  • 数理モデル概論

    2015.10 - 2016.3   Second semester

  • 応用数学I(A)

    2015.4 - 2015.9   First semester

  • 数学IB

    2015.4 - 2015.9   First semester

  • 情報数学特論１

    2014.10 - 2015.3   Second semester

  • 数学IC（建）

    2014.10 - 2015.3   Second semester

  • 数学IB（機航C）

    2014.10 - 2015.3   Second semester

  • 応用数学Ⅳ(D)

    2014.10 - 2015.3   Second semester

  • 応用数学I(A)

    2014.4 - 2014.9   First semester

  • 微分積分学

    2012.10 - 2013.3   Second semester

  • 応用数学Ⅳ(D)

    2012.10 - 2013.3   Second semester

  • 微分積分学

    2012.4 - 2012.9   First semester

  • 機能数理学概論I

    2012.4 - 2012.9   First semester

  • 機能数理学概論I

    2012.4 - 2012.9   First semester

  • 計算機数学概論

    2012.4 - 2012.9   First semester

  • 応用数学I(A)

    2012.4 - 2012.9   First semester

  • 応用数学Ⅳ(D)

    2011.10 - 2012.3   Second semester

  • 微分積分学

    2011.4 - 2011.9   First semester

  • 応用数学I(A)

    2011.4 - 2011.9   First semester

  • 数理統計学

    2011.4 - 2011.9   First semester

  • 機能数理学概論I

    2011.4 - 2011.9   First semester

  • 計算機数学概論

    2011.4 - 2011.9   First semester

  • 応用数学Ⅳ(D)

    2010.10 - 2011.3   Second semester

  • 応用数学I(A)

    2010.4 - 2010.9   First semester

  • 微分積分学

    2010.4 - 2010.9   First semester

  • 数学展望

    2010.4 - 2010.9   First semester

  • 情報数学B・演習

    2010.4 - 2010.9   First semester

  • 情報数学特論Ｃ（計算理論）

    2009.10 - 2010.3   Second semester

  • 応用数学Ⅳ(D)

    2009.10 - 2010.3   Second semester

  • 情報数学B・演習

    2009.4 - 2009.9   First semester

  • 応用数学I(A)

    2009.4 - 2009.9   First semester

  • 線形代数・同演習

    2009.4 - 2009.9   First semester

  • 情報数学特論Ｃ（計算理論）

    2008.10 - 2009.3   Second semester

  • 数学特論C5（情報）

    2008.10 - 2009.3   Second semester

  • 応用数学Ⅳ(D)

    2008.4 - 2008.9   First semester

  • 情報数学B

    2008.4 - 2008.9   First semester

  • 応用数学Ⅳ(D)

    2007.4 - 2007.9   First semester

  • 応用数学Ⅳ

    2006.4 - 2006.9   First semester

  • 複雑システム

    2006.4 - 2006.9   First semester

  • 応用数学Ⅳ

    2005.4 - 2005.9   First semester

  • 応用数学Ⅳ

    2004.4 - 2004.9   First semester

  • 計算数理学II (アルゴリズム基礎)

    2003.10 - 2004.3   Second semester

  • 数IB

    2003.10 - 2004.3   Second semester

  • 応用数学Ⅳ

    2002.4 - 2002.9   First semester

▼display all

Visiting, concurrent, or part-time lecturers at other universities, institutions, etc.

  • 2007  Queensland Univ. (Australia)  Classification:Affiliate faculty  Domestic/International Classification:Overseas 

    Semester, Day Time or Duration：2006年8月から2007年9月まで．

  • 2006  Queensland Univ. (Australia)  Classification:Affiliate faculty  Domestic/International Classification:Overseas 

    Semester, Day Time or Duration：2006年8月から2007年9月まで．

  • 2005  京都大学化学研究所  Classification:Affiliate faculty  Domestic/International Classification:Japan 

    Semester, Day Time or Duration：1年間

Outline of Social Contribution and International Cooperation activities

  • なし．

Social Activities

  • 来場者と研究者のダイレクトな双方向交流を通して、ゲノム研究に対する社会認識の現状を把握すること．

    文部科学省科学研究費特定領域研究ゲノム４領域  福岡市天神エルガーラ  2004.7

    　More details

    Audience：General,　Scientific,　Company,　Civic organization,　Governmental agency

    Type：Lecture

  • 来場者と研究者のダイレクトな双方向交流を通して、ゲノム研究に対する社会認識の現状を把握すること．

    文部科学省科学研究費特定領域研究ゲノム４領域  福岡市天神エルガーラ  2003.11

    　More details

    Audience：General,　Scientific,　Company,　Civic organization,　Governmental agency

    Type：Lecture

  • 来場者と研究者のダイレクトな双方向交流を通して、ゲノム研究に対する社会認識の現状を把握すること．

    文部科学省科学研究費特定領域研究ゲノム４領域  福岡市天神エルガーラ  2002.11

    　More details

    Audience：General,　Scientific,　Company,　Civic organization,　Governmental agency

    Type：Lecture

Travel Abroad

  • 2020.2

    Staying countory name 1：Philippines   Staying institution name 1：University of Philippines (UP) Diliman校

    Staying institution name 2：University of Philippines (UP) Manila校

  • 2019.10 - 2020.11

    Staying countory name 1：Greece   Staying institution name 1：BIBE2019

  • 2019.7

    Staying countory name 1：Switzerland   Staying institution name 1：ISMB2019

  • 2018.11

    Staying countory name 1：Philippines   Staying institution name 1：Philppine大学Manila校

    Staying institution name 2：Ateneo de Manila 大学

  • 2006.7 - 2007.9

    Staying countory name 1：Australia   Staying institution name 1：Queensland Univ.