Books

Papers

1.	Osamu Maruyama Yinuo Li Hiroki Narita Hidehiro Toh Wan Kin Au Yeung Hiroyuki Sasaki, CMIC: predicting DNA methylation inheritance of CpG islands with embedding vectors of variable-length k-mers, BMC Bioinformatics, 10.1186/s12859-022-04916-3, 23, 371, 2022.09, [URL], 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..
2.	Wan Kin Au Yeung, Osamu Maruyama, Hiroyuki Sasaki, A convolutional neural network-based regression model to infer the epigenetic crosstalk responsible for CG methylation patterns., BMC Bioinform. , 10.1186/s12859-021-04272-8, 22, 341-341, 2021.06, [URL].
3.	Osamu Maruyama, Fumiko Matsuzaki, DegSampler3: Pairwise Dependency Model in Degradation Motif Site Prediction of Substrate Protein Sequences, Proc. of 19th IEEE International Conference on Bioinformatics and Bioengineering, 2019.10.
4.	Osamu Maruyama,Fumiko Matsuzaki, DegSampler: Collapsed Gibbs sampler for detecting E3 binding sites, 18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018 Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018, 10.1109/BIBE.2018.00009, 1-9, 2018.12, 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..
5.	Osamu Maruyama, Yuki Kuwahara, RocSampler: Regularizing Overlapping Protein Complexes in Protein-Protein Interaction Networks, BMC Bioinformatics, 10.1186/s12859-017-1920-5, 18, 51-62, 491, 2017.12, [URL], 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..
6.	Osamu Maruyama, Limsoon Wong, Regularizing predicted complexes by mutually exclusive protein-protein interactions, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 1068-1075, 2015.08, 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..
7.	So Kobiki, Osamu Maruyama, ReSAPP: Predicting overlapping protein complexes by merging multiple-sampled partitions of proteins, Journal of bioinformatics and computational biology, 12, 6, 1442004, 2014.12.
8.	Chern Han Yong, Osamu Maruyama, Limsoon Wong, Discovery of small protein complexes from PPI networks with size-specific supervised weighting, BMC systems biology 8, S3-S3, 2014., 2014.12.
9.	Osamu Maruyama, Shota Shikita, A scale-free structure prior for Bayesian inference of Gaussian graphical models, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014. , 2014.11.
10.	Chasanah Kusumastuti Widita, Osamu Maruyama, PPSampler2: Predicting Protein Complexes More Accurately and Efficiently by Sampling, BMC Systems Biology, 7, Suppl 6, S14, 2013.12, 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..
11.	Daisuke Tatsuke, Osamu Maruyama, Sampling Strategy for Protein Complex Prediction Using Cluster Size Frequency, Gene, Special issue of the 23rd International Conference on Genome Informatics (GIW), 2012.12, 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..
12.	Osamu Maruyama, Heterodimeric Protein Complex Identification, ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011, 2011.08.
13.	Osamu Maruyama and Ayaka Chihara, NWE: Node-Weighted Expansion for Protein Complex Prediction Using Random Walk Distances, Proc. IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2010), 590-594, 2010.12.
14.	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, X chromosome-wide analyses of genomic DNA methylation states and gene expression in male and female neutrophils , Proceedings of the National Academy of Sciences of the United States of America (PNAS), 107, 3704-3709, 2010.02.
15.	Osamu Maruyama, Hideki Hirakawa, Takao Iwayanagi, Yoshiko Ishida, Shizu Takeda, Jun Otomo, Satoru Kuhara, Evaluating Protein Sequence Signatures Inferred from Protein-Protein Interaction Data by Gene Ontology Annotations, 2008 IEEE International Conference on Bioinformatics and Biomedicine, 417-420, 2008.11.
16.	Osamu Maruyama, Akiko Matsuda, and Satoru Kuhara, Reconstructing phylogenetic trees of prokaryote genomes by randomly sampling oligopeptides, 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.
17.	Daichi Shigemizu and Osamu Maruyama., Searching for Regulatory Elements of Alternative Splicing Events Using Phylogenetic Footprinting,, Proceedings of the 4th Workshop on Algorithms in Bioinformatics, Lecture Notes in Bioinformatics 3240, Springer-Verlag, 3240, 147-158, 147-158, 2004.09.
18.	Osamu Maruyama, Extensive Search for Discriminative Features of Alternative Splicing, Pacific Symposium on Biocomputing 2004, 54-65, 54-65, 2004.01.
19.	Osamu Maruyama, Finding optimal degenerate patterns in DNA sequences, Bioinformatics, 10.1093/bioinformatics/btg1079, 19, II206-II214, 19(supplement 2), 206-214, 2003.09.

Works, Software and Database

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Presentations

1.	Osamu Maruyama, Fumiko Matsuzaki, DegSampler3: Pairwise Dependency Model in Degradation Motif Site Prediction of Substrate Protein Sequences, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, 2019.10, [URL].
2.	Osamu Maruyama, Fumiko Matsuzaki, DegSampler: Collapsed Gibbs Sampler for Detecting E3 Binding Sites, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), 2018.12, 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..
3.	Osamu Maruyama, Limsoon Wong, Regularizing predicted complexes by mutually exclusive protein-protein interactions, International Symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics, HI-BI-BI 2015, 2015.08, 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..
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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)