Details of a Researcher - Sakakibara, Yasubumi

Sakakibara, Yasubumi

写真a

Affiliation: Faculty of Science and Technology, Department of Biosciences and Informatics （Yagami）

Position: Professor

Related Websites

http://www.dna.bio.keio.ac.jp/

External Links

Career 【 Display / hide 】

1985.04

-

1996.03

（株）富士通研究所情報社会科学研究所 ,研究員
1992.06

-

1993.06

兼米国カリフォルニア大学サンタクルーズ校 ,客員研究員
1996.04

-

2001.09

東京電機大学理工学部情報科学科 ,助教授
2001.10

-

2002.03

東京電機大学理工学部情報科学科 ,教授
2002.04

-

2005.03

慶應義塾大学理工学部生命情報学科 ,助教授

Academic Background 【 Display / hide 】

1983.03

Tokyo Institute of Technology, Faculty of Science, 情報科学科

University, Graduated
1985.03

Tokyo Institute of Technology, Graduate School, Division of Science and Engineeri, 情報科学専攻

Graduate School, Completed, Master's course

Academic Degrees 【 Display / hide 】

理学, Tokyo Institute of Technology, 1991.10

Research Areas 【 Display / hide 】

Life Science / System genome science
Informatics / Life, health and medical informatics (Bioinformatics)

Research Keywords 【 Display / hide 】

cancer genome
Bioinformatics

Research Themes 【 Display / hide 】

Big daya analysis,

2012.04

-

Present
Cancer genmome analysis,

2010.04

-

Present
functional RNA sequence analysis,

2002.04

-

Present

Books 【 Display / hide 】

情報数理シリーズ「計算論的学習」

榊原康文，小林聡，横森貴, 培風館, 2001.10

　View Summary

コンピュータに学習能力を持たせることを目的とする人工知能分野における機械学習の理論を解説する日本ではじめての教科書．
DNAコンピューティング

Paun, Rozenberg, Salomaa 著，横森貴, 榊原康文, 小林聡訳, シュプリンガー・フェアラーク東京, 1999.12

　View Summary

DNAコンピュータに関するはじめての教科書．形式言語理論に基づいたDNAコンピュータの理論に関する研究を中心に書かれている．
新版　情報処理ハンドブック

情報処理学会偏，13編6章の「学習」に関する節を分担執筆, オーム社, 1995.11

　View Summary

情報処理に関するすべての分野をカバーするハンドブック．

Papers 【 Display / hide 】

Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning

Akiyama M., Sakakibara Y.

NAR Genomics and Bioinformatics （NAR Genomics and Bioinformatics) 4 （ 1 ） 2022.03

　View Summary

Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-Training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-Training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this 'informative base embedding' and use it to achieve accuracies superior to those of existing state-of-The-Art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman-Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n2) instead of the O(n6) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.
- Access to Document (DOI)
Deep learning integration of molecular and interactome data for protein–compound interaction prediction

Watanabe N., Ohnuki Y., Sakakibara Y.

Journal of Cheminformatics （Journal of Cheminformatics) 13 （ 1 ） 2021.12

　View Summary

Motivation: Virtual screening, which can computationally predict the presence or absence of protein–compound interactions, has attracted attention as a large-scale, low-cost, and short-term search method for seed compounds. Existing machine learning methods for predicting protein–compound interactions are largely divided into those based on molecular structure data and those based on network data. The former utilize information on proteins and compounds, such as amino acid sequences and chemical structures; the latter rely on interaction network data, such as protein–protein interactions and compound–compound interactions. However, there have been few attempts to combine both types of data in molecular information and interaction networks. Results: We developed a deep learning-based method that integrates protein features, compound features, and multiple types of interactome data to predict protein–compound interactions. We designed three benchmark datasets with different difficulties and applied them to evaluate the prediction method. The performance evaluations show that our deep learning framework for integrating molecular structure data and interactome data outperforms state-of-the-art machine learning methods for protein–compound interaction prediction tasks. The performance improvement is statistically significant according to the Wilcoxon signed-rank test. This finding reveals that the multi-interactome data captures perspectives other than amino acid sequence homology and chemical structure similarity and that both types of data synergistically improve the prediction accuracy. Furthermore, experiments on the three benchmark datasets show that our method is more robust than existing methods in accurately predicting interactions between proteins and compounds that are unseen in training samples.
- Access to Document (DOI)
RNA secondary structure prediction using deep learning with thermodynamic integration

Sato K., Akiyama M., Sakakibara Y.

Nature Communications （Nature Communications) 12 （ 1 ） 2021.12

　View Summary

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.
- Access to Document (DOI)
Genomic style: yet another deep-learning approach to characterize bacterial genome sequences

Yoshimura Y, Hamada A, Augey Y, Akiyama M, Sakakibara Y.

Bioinformatics Advances （Oxford University Press) 1 （ 1 ） vbab039 2021.12

Last author, Corresponding author, Accepted
A max-margin model for predicting residue—base contacts in protein–rna interactions

Kashiwagi S., Sato K., Sakakibara Y.

Life （Life) 11 （ 11 ） 2021.11

　View Summary

Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data.
- Access to Document (DOI)

Papers, etc., Registered in KOARA 【 Display / hide 】

Analysis and control of cell function based on novel glycomics using saccharide primer method

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2015
Exploitation of pathogenic gastrointestinal bacteria specific molecular targeting therapy

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2015
Comprehensive analysis of functional non-coding RNAs in cancer genome on multistage carcinogenesis

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2014
Mechanisms of the development of neuronal cell community in the central nervous system

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2013
Mechanisms of the development of neuronal cell community in the central nervous system

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2010

Reviews, Commentaries, etc. 【 Display / hide 】

Guest Editorial for the 16th Asia Pacific Bioinformatics Conference

Yamanishi Y., Sakakibara Y., Chen Y.

IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE/ACM Transactions on Computational Biology and Bioinformatics) 16 ( 1 ) 2019.01

ISSN 15455963
- Access to Document (DOI)

Presentations 【 Display / hide 】

MetaVelvet : An extension of Velvet assembler to de novo metagenome assembly from short sequence reads

Namiki, T., Hachiya, T., Tanaka, H., and SAKAKIBARA YASUBUMI

ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011 (Chicago, USA) ,

2011.08
,
Oral presentation (general)
Comprehensive analysis of small non-coding RNAs in medaka transcriptome by deep RNA-seq approach

Abe, M., Hase, S., Ogawa, M., Okada, Y., Sato, K., Saito, Y., and SAKAKIBARA YASUBUMI

RNA 2011 Sixteenth Annual Meeting of the RNA Society (Kyoto, Japan) ,

2011.06
,
Oral presentation (general)
Genome-wide detections of non-coding RNAs on Ciona intestinalis genome: from in silico search of snoRNA to full-length sequencing and expression analysis

Kawarama, J., Hase, S., Hachiya, T., Hotta, K., Sakakibara, Y.

5th International Tunicate Meeting (Okinawa, Japan) ,

2009.06
,
Oral presentation (general)
Discriminative Detection of Cis-acting Regulatory Variation from Location Data

Yuji Kawada, Yasubumi Sakakibara

The 4th Asia-Pacific Bioinformatics Conference (APBC2006), Taiwan (Taiwan) ,

2006.02
,
Oral presentation (general)
Intensive in vitro experiments of implementing and executing finite automata in test tube

Junna Kuramochi and Yasubumi Sakakibara

The 11th International Meeting on DNA Based Computers (DNA11), London, Ontario (London, Ontario) ,

2005.06
,
Oral presentation (general)

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Genomic-style feature extraction and de novo design and synthesis of DNA sequence using deep learning

2020.11

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2026.03

JST, CREST, Commissioned ｒesearch, Principal investigator
非コードRNA遺伝子をゲノムワイドに発見する汎用システム

2018.04

-

2023.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (A) , Principal investigator
Deep analysis of chemical communication space using artificial intelligence technology

2017.06

-

2022.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research on Innovative Areas, Principal investigator
ビッグデータ駆動型創薬システム研究拠点

2014

-

2018

戦略的研究基盤形成支援事業, Principal investigator
発がんゲノムにおける非コードRNAの網羅的機能解析

2011

-

2014

Grant-in-Aid for Scientific Research, Principal investigator

Intellectual Property Rights, etc. 【 Display / hide 】

核酸分子を用いた新規な情報解析方法およびそれを用いた核酸の解析方法

Date applied：特願2000-382449 2000.12

Patent
Data Sorting,Data Sorting Tree Creating,Derivative

Date issued： United State Patent,No.5787426 1998.07

Patent

Awards 【 Display / hide 】

東京電機大学研究振興会　教育奨励賞

榊原康文, 1999, 東京電機大学研究振興会, 「プログラム言語のマルチメディア教育」

Courses Taught 【 Display / hide 】

BIOPROGRAMMING 1

2022
BIOINFORMATICS

2022
BASIC LABORATORY COURSE IN BIOSCIENCES

2022
BACHELOR'S THESIS

2022
ADVANCED LABORATORY COURSE IN BIOSCIENCES AND INFORMATICS B

2022

Courses Previously Taught 【 Display / hide 】

Bioinformatics

Keio University

2015.04

-

2016.03
,
Spring Semester, Lecture, Lecturer outside of Keio, 50people
基礎生命実験

Keio University

2015.04

-

2016.03
,
Autumn Semester, Laboratory work/practical work/exercise, Outside own faculty (within Keio), 50people
システムバイオロジー特論

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Outside own faculty (within Keio), 50people
BioProgramming 2

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Lecturer outside of Keio, 50people
Algorithm and Information processing

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Within own ｆaculty, 50people

Social Activities 【 Display / hide 】

第８回新しい RNA/RNP を見つける会

2009.09
6th International Workshop on Algorithmic Learning

1995.10

-

Present
（財）新世代コンピュータ技術開発機構蛋白質立体構造予測タスクグループ

1994.04

-

1995.03
2nd,3rd,4th,5th,6th International Colloquium on Gr

1994

-

2002
5th,7th,8th,9th,11th International Workshop on Alg

1994

-

2000

Memberships in Academic Societies 【 Display / hide 】

16th International Conference on DNA Computing and Molecular Programming,

2010.07
20th International Conference on Genome Informatics(GIW2009),

2009.12
人工知能学会人工知能基礎論研究会,

1998.04

-

2002.03
情報処理学会論文誌編集委員会,

1998.04

-

2002.03
第44回情報処理学会全国大会　チュートリアルセッション　機械学習入門,

1992

-

Present

Committee Experiences 【 Display / hide 】

2010.07

Program Committee Co-Chairman, 16th International Conference on DNA Computing and Molecular Programming
2009.12

organizing chairman, 20th International Conference on Genome Informatics(GIW2009)
2009.09

Committee Chair, 第８回新しい RNA/RNP を見つける会
1998.04

-

2002.03

幹事, 人工知能学会人工知能基礎論研究会
1998.04

-

2002.03

委員, 情報処理学会論文誌編集委員会