World's Best Scientists 2026 revealed!
Tatsuya Akutsu

Tatsuya Akutsu

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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
63
Citations
15762
World Ranking
2757
National Ranking
18

Tatsuya Akutsu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tatsuya Akutsu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 469 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tatsuya Akutsu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tatsuya Akutsu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Tatsuya Akutsu is a researcher affiliated with Kyoto University in Japan. Their work spans multiple disciplines, with a primary focus on biochemistry, genetics, molecular biology, and computer science. They have contributed extensively to subfields including molecular biology, computational theory and mathematics, artificial intelligence, materials chemistry, and spectroscopy.

The main topics covered in their research include:

  • Computational Drug Discovery Methods
  • Gene Regulatory Network Analysis
  • Machine Learning in Bioinformatics
  • Protein Structure and Dynamics
  • Bioinformatics and Genomic Networks
  • Machine Learning in Materials Science
  • RNA and protein synthesis mechanisms

Tatsuya Akutsu has published a significant number of papers in various venues. The most frequent publication platforms include:

  • arXiv (Cornell University)
  • Briefings in Bioinformatics
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • BMC Bioinformatics
  • Scientific Reports

Their recent publications demonstrate an emphasis on machine learning applications in bioinformatics and related fields. Some of these notable papers are:

  • iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, 2021, Nucleic Acids Research
  • Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information, 2020, Genomics Proteomics & Bioinformatics
  • iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets, 2022, Nucleic Acids Research
  • DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy, 2020, Briefings in Bioinformatics
  • iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities, 2023, Briefings in Bioinformatics

Their collaborations involve frequent co-authorship with researchers such as Jianshen Zhu, Hiroshi Nagamochi, Naveed Ahmed Azam, Jiangning Song, and Kazuya Haraguchi, with collaboration counts ranging from 19 to 25 publications each.

Best Publications

  • Identification of genetic networks from a small number of gene expression patterns under the Boolean network model.

    Tatsuya Akutsu;Satoru Miyano;Satoru Kuhara

  • Control of Boolean networks: hardness results and algorithms for tree structured networks.

    Tatsuya Akutsu;Morihiro Hayashida;Wai-Ki Ching;Michael K. Ng

  • Inferring qualitative relations in genetic networks and metabolic pathways

    Tatsuya Akutsu;Satoru Miyano;Satoru Kuhara

  • Protein homology detection using string alignment kernels

    Hiroto Saigo;Jean-Philippe Vert;Nobuhisa Ueda;Tatsuya Akutsu

  • Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots

    Tatsuya Akutsu

  • iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

    Zhen Chen;Pei Zhao;Fuyi Li;Tatiana T Marquez-Lago

  • IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

    Kengo Sato;Yuki Kato;Michiaki Hamada;Tatsuya Akutsu

  • Extensions of marginalized graph kernels

    Pierre Mahé;Nobuhisa Ueda;Tatsuya Akutsu;Jean-Luc Perret

  • Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

    Tatsuya Akutsu;Satoru Miyano;Satoru Kuhara

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

    Zhen Chen;Pei Zhao;Chen Li;Fuyi Li;Fuyi Li

  • Graph Kernels for Molecular Structure−Activity Relationship Analysis with Support Vector Machines

    Pierre Mahé;Nobuhisa Ueda;Tatsuya Akutsu;Jean-Luc Perret

  • iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

    Jiangning Song;Yanan Wang;Fuyi Li;Tatsuya Akutsu

  • Dominating scale-free networks with variable scaling exponent: heterogeneous networks are not difficult to control

    Jose C Nacher;Jose C Nacher;Tatsuya Akutsu

  • A novel representation of protein sequences for prediction of subcellular location using support vector machines

    Setsuro Matsuda;Jean Philippe Vert;Hiroto Saigo;Nobuhisa Ueda

  • Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions

    Tatsuya Akutsu;Satoru Kuhara;Osamu Maruyama;Satoru Miyano

  • Cascleave: towards more accurate prediction of caspase substrate cleavage sites.

    Jiangning Song;Hao Tan;Hongbin Shen;Khalid Mahmood

  • A System for Identifying Genetic Networks from Gene Expression Patterns Produced by Gene Disruptions and Overexpressions.

    Tatsuya Akutsu;Satoru Kuhara;Osamu Maruyama;Satoru Miyano

  • Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    Fuyi Li;Chen Li;Chen Li;Tatiana T Marquez-Lago;André Leier

  • A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

    Shutao Mei;Fuyi Li;André Leier;Tatiana T Marquez-Lago

  • PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

    Jiangning Song;Fuyi Li;Andre Leier;Tatiana Marquez-Lago

  • Algorithms for inferring qualitative models of biological networks.

    Tatsuya Akutsu;Satoru Miyano;Satoru Kuhara

Frequent Co-Authors

Jiangning Song
Jiangning Song Monash University
Wai-Ki Ching
Wai-Ki Ching University of Hong Kong
Hiroshi Nagamochi
Hiroshi Nagamochi Kyoto University
Satoru Miyano
Satoru Miyano Tokyo Medical and Dental University
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Minoru Kanehisa
Minoru Kanehisa Kyoto University
André Leier
André Leier University of Alabama at Birmingham
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Satoru Kuhara
Satoru Kuhara Kyushu University
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)

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