World's Best Scientists 2026 revealed!
Yoshihiro Yamanishi

Yoshihiro Yamanishi

D-Index & Metrics

Computer Science

D-Index
38
Citations
14228
World Ranking
9951
National Ranking
147

Yoshihiro Yamanishi 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 Yoshihiro Yamanishi 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: 108 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Yoshihiro Yamanishi 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 Yoshihiro Yamanishi 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Yoshihiro Yamanishi is affiliated with Nagoya University in Japan. Their research predominantly spans the fields of Biochemistry, Genetics and Molecular Biology as well as Computer Science.

Their work focuses on a variety of specialized subfields including Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Pharmacology, and Oncology.

The main topics covered in their research involve Computational Drug Discovery Methods, Machine Learning in Materials Science, Bioinformatics and Genomic Networks, Protein Structure and Dynamics, vaccines and immunoinformatics approaches, Metabolomics and Mass Spectrometry Studies, and Microbial Natural Products and Biosynthesis.

Yamanishi has contributed frequently to a number of publication venues. These include:

  • Journal of Chemical Information and Modeling
  • Bioinformatics
  • Molecular Informatics
  • BMC Bioinformatics
  • iScience

Some of their recent papers are:

  • Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking, 2021, Journal of Chemical Information and Modeling
  • The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer, 2021, Cancer Science
  • Dual graph convolutional neural network for predicting chemical networks, 2020, BMC Bioinformatics
  • Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules, 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets, 2020, Bioinformatics

Their frequent coauthors include:

  • M Iwata
  • Ryusuke Sawada
  • Chen Li
  • Tomokazu Shibata
  • Momoko Hamano

Best Publications

  • KEGG for linking genomes to life and the environment

    Minoru Kanehisa;Michihiro Araki;Susumu Goto;Masahiro Hattori

  • Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

    Yoshihiro Yamanishi;Michihiro Araki;Alex Gutteridge;Wataru Honda

  • Supervised prediction of drug–target interactions using bipartite local models

    Kevin Bleakley;Yoshihiro Yamanishi

  • Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

    Yoshihiro Yamanishi;Masaaki Kotera;Minoru Kanehisa;Susumu Goto

  • Protein network inference from multiple genomic data: a supervised approach

    Y. Yamanishi;J.-P. Vert;M. Kanehisa

  • Predicting drug side-effect profiles: a chemical fragment-based approach

    Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Véronique Stoven;Véronique Stoven;Véronique Stoven;Yoshihiro Yamanishi;Yoshihiro Yamanishi;Yoshihiro Yamanishi

  • Relating drug–protein interaction network with drug side effects

    Sayaka Mizutani;Edouard Pauwels;Véronique Stoven;Susumu Goto

  • Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction

    Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama

  • The inference of protein–protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships

    Tetsuya Sato;Yoshihiro Yamanishi;Minoru Kanehisa;Hiroyuki Toh

  • Drug target prediction using adverse event report systems

    Masataka Takarabe;Masaaki Kotera;Yosuke Nishimura;Susumu Goto

  • Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis

    Yoshihiro Yamanishi;Jean-Philippe Vert;Akihiro Nakaya;Minoru Kanehisa

  • Drug side-effect prediction based on the integration of chemical and biological spaces.

    Yoshihiro Yamanishi;Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Masaaki Kotera

  • KEGG OC: A large-scale automatic construction of taxonomy-based ortholog clusters

    Akihiro Nakaya;Toshiaki Katayama;Masumi Itoh;Kazushi Hiranuka

  • DINIES: drug–target interaction network inference engine based on supervised analysis

    Yoshihiro Yamanishi;Masaaki Kotera;Yuki Moriya;Ryusuke Sawada

  • Supervised enzyme network inference from the integration of genomic data and chemical information

    Yoshihiro Yamanishi;Jean-Philippe Vert;Minoru Kanehisa

  • Supervised Graph Inference

    Jean-philippe Vert;Yoshihiro Yamanishi

  • Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers

    Yasuo Tabei;Edouard Pauwels;Edouard Pauwels;Edouard Pauwels;Véronique Stoven;Véronique Stoven;Véronique Stoven;Kazuhiro Takemoto;Kazuhiro Takemoto

  • E-zyme

    Yoshihiro Yamanishi;Masahiro Hattori;Masaaki Kotera;Susumu Goto

  • Extracting sets of chemical substructures and protein domains governing drug-target interactions

    Yoshihiro Yamanishi;Edouard Pauwels;Hiroto Saigo;Véronique Stoven

  • Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data

    Hiroaki Iwata;Ryusuke Sawada;Sayaka Mizutani;Yoshihiro Yamanishi

Frequent Co-Authors

Minoru Kanehisa
Minoru Kanehisa Kyoto University
Susumu Goto
Susumu Goto Osaka University
Hiroyuki Toh
Hiroyuki Toh Kwansei Gakuin University
Hisashi Kashima
Hisashi Kashima Kyoto University
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)
Koji Tsuda
Koji Tsuda University of Tokyo
Rasmus Pagh
Rasmus Pagh University of Copenhagen
Simon J. Puglisi
Simon J. Puglisi University of Helsinki
Kenzaburo Tani
Kenzaburo Tani University of Tokyo
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University

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