World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
73
Citations
22861
World Ranking
1580
National Ranking
825

Jean-Philippe Vert 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 Jean-Philippe Vert 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: 239 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Jean-Philippe Vert 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 Jean-Philippe Vert 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: 73 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Overview

Jean-Philippe Vert is affiliated with Google in the United States and has contributed extensively to the fields of biochemistry, genetics, and molecular biology. Their research addresses multiple subfields including molecular biology, artificial intelligence, biophysics, cancer research, and genetics.

Their recent publications demonstrate a focus on single-cell analysis, gene regulation, and applications of AI in biotechnology. Notable recent papers include:

  • Transcriptional Programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution, 2020, Cell Reports
  • DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer, 2022, Nature Biotechnology
  • Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference, 2020, Bioinformatics
  • AI-based mobile application to fight antibiotic resistance, 2021, Nature Communications
  • How will generative AI disrupt data science in drug discovery?, 2023, Nature Biotechnology

The main research topics in Jean-Philippe Vert's work include:

  • Single-cell and spatial transcriptomics
  • Cell image analysis techniques
  • RNA and protein synthesis mechanisms
  • Genomics and chromatin dynamics
  • Genomics and phylogenetic studies
  • Gene regulatory network analysis
  • Gene expression and cancer classification

Jean-Philippe Vert frequently publishes in the following venues:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Bioinformatics
  • Genome Biology

Frequent collaborators in their publications include:

  • William Stafford Noble
  • Félix Raimundo
  • Céline Vallot
  • Ran Zhang
  • Quentin Berthet

Jean-Philippe Vert's research integrates advanced computational techniques with molecular and cellular biological data. Their work often applies artificial intelligence tools to explore complex biological systems, gene regulation, and cancer heterogeneity at the single-cell level. This blend of disciplines reflects a commitment to developing new analytical frameworks for understanding molecular and genetic mechanisms in health and disease.

Best Publications

  • HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

    Nicolas Servant;Nelle Varoquaux;Nelle Varoquaux;Nelle Varoquaux;Bryan R. Lajoie;Eric Viara

  • Group lasso with overlap and graph lasso

    Laurent Jacob;Guillaume Obozinski;Jean-Philippe Vert

  • Kernel Methods in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • A general and flexible method for signal extraction from single-cell RNA-seq data

    Davide Risso;Fanny Perraudeau;Svetlana Gribkova;Sandrine Dudoit

  • Protein-ligand interaction prediction

    Laurent Jacob;Jean-Philippe Vert

  • Protein homology detection using string alignment kernels

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

  • Clustered Multi-Task Learning: A Convex Formulation

    Laurent Jacob;Jean-philippe Vert;Francis R. Bach

  • TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

    Anne-Claire Haury;Fantine Mordelet;Paola Vera-Licona;Paola Vera-Licona;Paola Vera-Licona;Jean-Philippe Vert;Jean-Philippe Vert;Jean-Philippe Vert

  • The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures

    Anne-Claire Haury;Pierre Gestraud;Jean-Philippe Vert

  • A Path Following Algorithm for the Graph Matching Problem

    M. Zaslavskiy;F. Bach;J.-P. Vert

  • A Primer on Kernel Methods

    JP Vert;K Tsuda;B Schölkopf;B. Schölkopf K. Tsuda

  • Support Vector Machine Applications in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • An accurate and interpretable model for siRNA efficacy prediction

    Jean-Philippe Vert;Nicolas Foveau;Christian Lajaunie;Yves Vandenbrouck

  • Consistency of Random Forests

    Erwan Scornet;Gérard Biau;Jean-Philippe Vert

  • A bagging SVM to learn from positive and unlabeled examples

    F. Mordelet;J. P. Vert;J. P. Vert;J. P. Vert

  • Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

    Valentina Boeva;Andrei Zinovyev;Kevin Bleakley;Jean-Philippe Vert

  • Classification of microarray data using gene networks

    Franck Rapaport;Franck Rapaport;Andrei Yu. Zinovyev;Marie Dutreix;Emmanuel Barillot

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

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

  • A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

    Jacob Abernethy;Francis Bach;Theodoros Evgeniou;Jean-Philippe Vert

  • A statistical approach for inferring the 3D structure of the genome

    Nelle Varoquaux;Ferhat Ay;William Stafford Noble;Jean Philippe Vert

  • Supervised reconstruction of biological networks with local models

    Kevin Bleakley;Gérard Biau;Jean-Philippe Vert

Frequent Co-Authors

Francis Bach
Francis Bach École Normale Supérieure
William Stafford Noble
William Stafford Noble University of Washington
Koji Tsuda
Koji Tsuda University of Tokyo
Marco Cuturi
Marco Cuturi École Nationale de la Statistique et de l'Administration Économique
Emmanuel Barillot
Emmanuel Barillot Institute Curie
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Andrei Zinovyev
Andrei Zinovyev Institute Curie
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Ferhat Ay
Ferhat Ay La Jolla Institute For Allergy & Immunology
Minoru Kanehisa
Minoru Kanehisa Kyoto University

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