World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
10721
World Ranking
3695
National Ranking
1763

Daisuke Kihara 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 Daisuke Kihara 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: 266 publications — 66th percentile

66% of scientists in this discipline score the same or lower.

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

Daisuke Kihara 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 Daisuke Kihara 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: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Daisuke Kihara is affiliated with Purdue University West Lafayette in the United States and has contributed extensively to the field of biochemistry, genetics, and molecular biology. Their research primarily focuses on the molecular biology and structural biology subfields, with significant involvement in materials chemistry, computational theory and mathematics, and computer vision and pattern recognition.

The scientist's work covers several main topics, including protein structure and dynamics, enzyme structure and function, advanced electron microscopy techniques and applications, RNA and protein synthesis mechanisms, machine learning in bioinformatics, genomics and phylogenetic studies, and computational drug discovery methods.

Frequent publication venues for Daisuke Kihara include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Biophysical Journal
  • Methods in molecular biology
  • arXiv (Cornell University)

Among recent papers, several notable publications are:

  • Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment, 2021, Proteins Structure Function and Bioinformatics
  • Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge, 2021, Nature Methods
  • LZerD webserver for pairwise and multiple protein-protein docking, 2021, Nucleic Acids Research
  • Protein folds vs. protein folding: Differing questions, different challenges, 2022, Proceedings of the National Academy of Sciences
  • Protein Docking Model Evaluation by Graph Neural Networks, 2021, Frontiers in Molecular Biosciences

Daisuke Kihara has collaborated frequently with several coauthors, including:

  • Genki Terashi
  • Xiao Wang
  • Charles Christoffer
  • Yuki Kagaya
  • Tunde Aderinwale

Best Publications

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Limitations and potentials of current motif discovery algorithms

    Jianjun Hu;Bin Li;Daisuke Kihara

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T Clark;Asma R Bankapur

  • Protein-protein docking using region-based 3D Zernike descriptors

    Vishwesh Venkatraman;Yifeng D Yang;Lee Sael;Daisuke Kihara

  • Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm.

    Jeffrey Skolnick;Daisuke Kihara;Yang Zhang

  • Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding.

    Yang Zhang;Daisuke Kihara;Jeffrey Skolnick

  • Enhanced automated function prediction using distantly related sequences and contextual association by PFP

    Troy Hawkins;Stanislav Luban;Daisuke Kihara

  • Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

    Marc F. Lensink;Sameer Velankar;Andriy Kryshtafovych;Shen You Huang

  • PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

    Troy Hawkins;Meghana Chitale;Stanislav Luban;Daisuke Kihara

  • Defrosting the frozen approximation: PROSPECTOR--a new approach to threading.

    Jeffrey Skolnick;Daisuke Kihara

  • Community-wide assessment of protein-interface modeling suggests improvements to design methodology

    Sarel J. Fleishman;Sarel J. Fleishman;Timothy A. Whitehead;Eva Maria Strauch;Jacob E. Corn;Jacob E. Corn

  • Fast protein tertiary structure retrieval based on global surface shape similarity.

    Lee Sael;Bin Li;David La;Yi Fang

  • De novo main-chain modeling for EM maps using MAINMAST

    Genki Terashi;Daisuke Kihara

  • Function prediction of uncharacterized proteins.

    Troy Hawkins;Daisuke Kihara

  • Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

    Marc F. Lensink;Guillaume Brysbaert;Nurul Nadzirin;Sameer Velankar

  • ESG: Extended Similarity Group method for automated protein function prediction

    Meghana Chitale;Troy Hawkins;Changsoon Park;Daisuke Kihara

  • ESG: extended similarity group method for automated protein function prediction

    Meghana Chitale;Troy Hawkins;Changsoon Park;Daisuke Kihara

  • Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

    Marc F Lensink;Guillaume Brysbaert;Théo Mauri;Nurul Nadzirin

  • Potential for Protein Surface Shape Analysis Using Spherical Harmonics and 3D Zernike Descriptors

    Vishwesh Venkatraman;Lee Sael;Daisuke Kihara

  • Protein docking model evaluation by 3D deep convolutional neural networks.

    Xiao Wang;Genki Terashi;Charles W Christoffer;Mengmeng Zhu

  • Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

Frequent Co-Authors

Andrzej Kolinski
Andrzej Kolinski University of Warsaw
Jeffrey Skolnick
Jeffrey Skolnick Georgia Institute of Technology
Jianlin Cheng
Jianlin Cheng University of Missouri
Christophe Dessimoz
Christophe Dessimoz University College London
Juan Fernández-Recio
Juan Fernández-Recio Spanish National Research Council
Shoshana J. Wodak
Shoshana J. Wodak Vrije Universiteit Brussel
David T. Jones
David T. Jones University College London
Tapio Salakoski
Tapio Salakoski University of Turku
Paul A. Bates
Paul A. Bates The Francis Crick Institute
Brian G. Pierce
Brian G. Pierce University of Maryland, College Park

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