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David Gfeller

D-Index & Metrics

Biology and Biochemistry

D-Index
54
Citations
13762
World Ranking
15452
National Ranking
286

David Gfeller publication distribution in Biology and Biochemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Biology and Biochemistry in 2026. The highlighted bar marks where David Gfeller sits on this spectrum.

47–56 publications: 8 scientists 57–66 publications: 35 scientists 67–76 publications: 106 scientists 77–86 publications: 231 scientists 87–96 publications: 414 scientists 97–106 publications: 546 scientists 107–116 publications: 704 scientists 117–126 publications: 849 scientists 127–136 publications: 980 scientists 137–146 publications: 942 scientists 147–156 publications: 969 scientists 157–166 publications: 950 scientists 167–176 publications: 951 scientists 177–186 publications: 915 scientists 187–196 publications: 787 scientists 197–206 publications: 841 scientists 207–216 publications: 735 scientists 217–226 publications: 709 scientists 227–236 publications: 651 scientists 237–246 publications: 605 scientists 247–256 publications: 510 scientists 257–266 publications: 524 scientists 267–276 publications: 434 scientists 277–286 publications: 418 scientists 287–296 publications: 350 scientists 297–306 publications: 363 scientists 307–316 publications: 315 scientists 317–326 publications: 296 scientists 327–336 publications: 261 scientists 337–346 publications: 240 scientists 347–356 publications: 219 scientists 357–366 publications: 197 scientists 367–376 publications: 154 scientists 377–386 publications: 161 scientists 387–396 publications: 155 scientists 397–406 publications: 145 scientists 407–416 publications: 124 scientists 417–426 publications: 112 scientists 427–436 publications: 132 scientists 437–446 publications: 116 scientists 447–456 publications: 99 scientists 457–466 publications: 81 scientists 467–476 publications: 91 scientists 477–486 publications: 80 scientists 487–496 publications: 80 scientists 497–506 publications: 60 scientists 507–516 publications: 36 scientists 517–526 publications: 46 scientists 527–536 publications: 54 scientists 537–546 publications: 44 scientists 547–556 publications: 43 scientists 557–566 publications: 43 scientists 567–576 publications: 42 scientists 577–586 publications: 25 scientists 587–596 publications: 34 scientists 597–606 publications: 23 scientists 607–616 publications: 33 scientists 617–626 publications: 31 scientists 627–636 publications: 27 scientists 637–646 publications: 25 scientists 647–656 publications: 28 scientists 657–666 publications: 34 scientists 667–676 publications: 18 scientists 677–686 publications: 16 scientists 687–696 publications: 10 scientists 697–706 publications: 12 scientists 707–716 publications: 21 scientists 717–726 publications: 12 scientists 727–736 publications: 12 scientists 737–746 publications: 10 scientists 747–756 publications: 7 scientists 757–766 publications: 13 scientists 767–776 publications: 15 scientists 777–786 publications: 13 scientists 787–796 publications: 9 scientists 797–806 publications: 9 scientists 807–816 publications: 7 scientists 817–826 publications: 4 scientists 827–836 publications: 9 scientists 837–846 publications: 7 scientists 847–856 publications: 3 scientists 857–866 publications: 5 scientists 867–876 publications: 5 scientists 877–886 publications: 11 scientists 887–896 publications: 3 scientists 897–906 publications: 4 scientists 907–916 publications: 7 scientists 917–926 publications: 5 scientists 927–936 publications: 6 scientists 937–946 publications: 6 scientists 947–956 publications: 3 scientists 957–966 publications: 7 scientists 967–976 publications: 2 scientists 977–986 publications: 2 scientists 987–996 publications: 1 scientists 997–1,006 publications: 5 scientists 1,007–1,016 publications: 2 scientists 1,017–1,026 publications: 2 scientists 1,027 publications: 1 scientists 1,028+ publications: 100 scientists
47 publications 1,028+

This scientist: 105 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,028 publications or more.

David Gfeller D-index placement in Biology and Biochemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Biology and Biochemistry scientists ranked by Research.com in 2026. The highlighted bar marks where David Gfeller sits on this spectrum.

40–41 D-Index: 80 scientists 42–43 D-Index: 183 scientists 44–45 D-Index: 317 scientists 46–47 D-Index: 504 scientists 48–49 D-Index: 718 scientists 50–51 D-Index: 900 scientists 52–53 D-Index: 1,026 scientists 54–55 D-Index: 1,150 scientists 56–57 D-Index: 1,236 scientists 58–59 D-Index: 1,253 scientists 60–61 D-Index: 1,163 scientists 62–63 D-Index: 1,131 scientists 64–65 D-Index: 1,032 scientists 66–67 D-Index: 897 scientists 68–69 D-Index: 814 scientists 70–71 D-Index: 715 scientists 72–73 D-Index: 709 scientists 74–75 D-Index: 596 scientists 76–77 D-Index: 512 scientists 78–79 D-Index: 473 scientists 80–81 D-Index: 412 scientists 82–83 D-Index: 373 scientists 84–85 D-Index: 358 scientists 86–87 D-Index: 285 scientists 88–89 D-Index: 273 scientists 90–91 D-Index: 227 scientists 92–93 D-Index: 208 scientists 94–95 D-Index: 193 scientists 96–97 D-Index: 153 scientists 98–99 D-Index: 157 scientists 100–101 D-Index: 148 scientists 102–103 D-Index: 120 scientists 104–105 D-Index: 113 scientists 106–107 D-Index: 100 scientists 108–109 D-Index: 86 scientists 110–111 D-Index: 67 scientists 112–113 D-Index: 72 scientists 114–115 D-Index: 73 scientists 116–117 D-Index: 64 scientists 118–119 D-Index: 53 scientists 120–121 D-Index: 60 scientists 122–123 D-Index: 54 scientists 124–125 D-Index: 43 scientists 126–127 D-Index: 38 scientists 128–129 D-Index: 49 scientists 130–131 D-Index: 26 scientists 132–133 D-Index: 18 scientists 134–135 D-Index: 23 scientists 136–137 D-Index: 32 scientists 138–139 D-Index: 32 scientists 140–141 D-Index: 27 scientists 142–143 D-Index: 19 scientists 144–145 D-Index: 22 scientists 146–147 D-Index: 12 scientists 148–149 D-Index: 16 scientists 150–151 D-Index: 14 scientists 152–153 D-Index: 10 scientists 154–155 D-Index: 13 scientists 156–157 D-Index: 10 scientists 158–159 D-Index: 7 scientists 160–161 D-Index: 9 scientists 162–163 D-Index: 13 scientists 164–165 D-Index: 4 scientists 166 D-Index: 4 scientists 167+ D-Index: 98 scientists
40 D-Index 167+

This scientist: 54 D-Index — 22nd percentile

22% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 167 D-Index or more.

Overview

David Gfeller is affiliated with the University of Lausanne in Switzerland. Their research predominantly spans the fields of Biochemistry, Genetics and Molecular Biology, Immunology and Microbiology, and Medicine. Within these broader fields, their work focuses on molecular biology, immunology, oncology, cancer research, and radiology, nuclear medicine, and imaging.

The main topics addressed in their research include immunotherapy and immune responses, vaccines and immunoinformatics approaches, T-cell and B-cell immunology, single-cell and spatial transcriptomics, CAR-T cell therapy research, cancer immunotherapy and biomarkers, and monoclonal and polyclonal antibodies research.

David Gfeller has been involved in publications across several venues, with frequent contributions to:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Science Advances
  • Nature Communications
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Biotechnology

Some of their recent papers include:

  • Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction, 2020, Cell
  • Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer, 2021, Science Advances
  • Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting, 2021, Cell Reports Medicine
  • Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression, 2021, Cancer Discovery
  • Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes, 2023, Immunity

Their frequent coauthors include:

  • Julien Racle
  • Alexandre Harari
  • George Coukos
  • Julien Schmidt
  • Giancarlo Croce

Best Publications

  • Intratumoral Tcf1+PD-1+CD8+ T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy.

    Imran Siddiqui;Karin Schaeuble;Vijaykumar Chennupati;Silvia A. Fuertes Marraco

  • Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

    Julien Racle;Julien Racle;Kaat de Jonge;Petra Baumgaertner;Daniel E Speiser

  • SwissTargetPrediction: A web server for target prediction of bioactive small molecules

    David Gfeller;Aurélien Grosdidier;Matthias Wirth;Antoine Daina

  • Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

    Daniel K. Wells;Marit M. van Buuren;Kristen K. Dang;Vanessa M. Hubbard-Lucey

  • Shaping the interaction landscape of bioactive molecules.

    David Gfeller;Olivier Michielin;Vincent Zoete

  • Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer

    Janos L. Tanyi;Sara Bobisse;Eran Ophir;Sandra Tuyaerts

  • EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.

    Julien Racle;David Gfeller;David Gfeller

  • Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer

    Amélie Cachot;Mariia Bilous;Mariia Bilous;Yen-Cheng Liu;Xiaokang Li

  • Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

    Michal Bassani-Sternberg;Chloé Chong;Chloé Chong;Philippe Guillaume;Philippe Guillaume;Marthe Solleder;Marthe Solleder

  • Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

    Julien Racle;Justine Michaux;Georg Alexander Rockinger;Marion Arnaud

  • Functional complexes between YAP2 and ZO-2 are PDZ domain-dependent, and regulate YAP2 nuclear localization and signalling

    Tsutomu Oka;Eline Remue;Kris Meerschaert;Berlinda Vanloo

  • Bayesian Modeling of the Yeast SH3 Domain Interactome Predicts Spatiotemporal Dynamics of Endocytosis Proteins

    Raffi Tonikian;Xiaofeng Xin;Christopher P. Toret;David Gfeller

  • Tumour-derived PGD2 and NKp30-B7H6 engagement drives an immunosuppressive ILC2-MDSC axis

    Sara Trabanelli;Mathieu F. Chevalier;Amaia Martinez-Usatorre;Alejandra Gomez-Cadena

  • High-throughput and Sensitive Immunopeptidomics Platform Reveals Profound Interferonγ-Mediated Remodeling of the Human Leukocyte Antigen (HLA) Ligandome.

    Chloe Chong;Fabio Marino;Fabio Marino;HuiSong Pak;HuiSong Pak;Julien Racle;Julien Racle;Julien Racle

  • Complex network analysis of free-energy landscapes.

    D. Gfeller;P. De Los Rios;A. Caflisch;F. Rao

  • ILC2-modulated T cell-to-MDSC balance is associated with bladder cancer recurrence

    Mathieu F. Chevalier;Sara Trabanelli;Julien Racle;Julien Racle;Bérengère Salomé

  • The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.

    David Gfeller;David Gfeller;Philippe Guillaume;Justine Michaux;Hui-Song Pak

  • Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

    David Gfeller;Michal Bassani-Sternberg

  • Finding instabilities in the community structure of complex networks.

    David Gfeller;Jean-Cédric Chappelier;Paolo De Los Rios

  • Spectral coarse graining of complex networks

    David Gfeller;Paolo De Los Rios

Frequent Co-Authors

George Coukos
George Coukos Cornell University
Michal Bassani-Sternberg
Michal Bassani-Sternberg Ludwig Cancer Research
Philippe Guillaume
Philippe Guillaume University of Lausanne
Olivier Michielin
Olivier Michielin University of Lausanne
Daniel E. Speiser
Daniel E. Speiser University of Lausanne
Alexandre Harari
Alexandre Harari University of Lausanne
Lana E. Kandalaft
Lana E. Kandalaft Ludwig Cancer Research
Sachdev S. Sidhu
Sachdev S. Sidhu University of Waterloo
Gary D. Bader
Gary D. Bader University of Toronto
Brian Stevenson
Brian Stevenson University of Kentucky

External Links

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