Sakakibara, Yasubumi

写真a

Affiliation

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

Position

Professor

Related Websites

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

    慶應義塾大学大学院理工学研究科基礎理工学専攻 ,助教授

display all >>

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.

  • 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.

  • 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

  • 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.

  • 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.

display all >>

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

display all >>

Reviews, Commentaries, etc. 【 Display / hide

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)

display all >>

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

  • Construction of chemical latent space using deep learning and design of artificial molecular structures

    2023.04
    -
    2028.03

    学術変革領域研究(A), Principal investigator

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

    2020.11
    -
    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

display all >>

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

  • TOPICS IN SYSTEMS BIOLOGY

    2023

  • TOPICS IN BIOSCIENCES AND INFORMATICS A

    2023

  • TOPICS IN BIOSCIENCES AND INFORMATICS 1

    2023

  • SEMINAR IN BIOSCIENCES AND INFORMATICS

    2023

  • METHODOLOGY FOR POST-GENOME BIOSCIENCES

    2023

display all >>

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

display all >>

 

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

display all >>

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

display all >>

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

display all >>