David Gfeller

13.6k total citations · 4 hit papers
73 papers, 6.8k citations indexed

About

David Gfeller is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, David Gfeller has authored 73 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 36 papers in Immunology and 17 papers in Oncology. Recurrent topics in David Gfeller's work include vaccines and immunoinformatics approaches (28 papers), Immunotherapy and Immune Responses (22 papers) and Monoclonal and Polyclonal Antibodies Research (15 papers). David Gfeller is often cited by papers focused on vaccines and immunoinformatics approaches (28 papers), Immunotherapy and Immune Responses (22 papers) and Monoclonal and Polyclonal Antibodies Research (15 papers). David Gfeller collaborates with scholars based in Switzerland, United States and Canada. David Gfeller's co-authors include Vincent Zoete, Olivier Michielin, Julien Racle, Daniel E. Speiser, Michal Bassani‐Sternberg, Aurélien Grosdidier, Matthias Wirth, Antoine Daina, Petra Baumgaertner and Kaat de Jonge and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nucleic Acids Research.

In The Last Decade

David Gfeller

71 papers receiving 6.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SwissTargetPrediction: a web server for target prediction of bioactive small molecules

2014 1.2k citations David Gfeller, Olivier Michielin et al. Nucleic Acids Research profile →
Intratumoral Tcf1+PD-1+CD8+ T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy

2019 942 citations Silvia A. Fuertes Marraco, Santiago J. Carmona et al. profile →
Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

2017 880 citations Julien Racle, Petra Baumgaertner et al. profile →
Shaping the interaction landscape of bioactive molecules

2013 369 citations David Gfeller, Olivier Michielin et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Gfeller Switzerland	38	3.8k	2.6k	2.0k	671	652	73	6.8k
Timothy G. Myers United States	45	5.8k 1.5×	1.1k 0.4×	2.1k 1.1×	255 0.4×	544 0.8×	103	9.3k
Maurizio Pellecchia United States	56	7.7k 2.0×	1.5k 0.6×	1.9k 1.0×	477 0.7×	856 1.3×	205	11.1k
András Fiser United States	43	8.0k 2.1×	1.2k 0.5×	1.4k 0.7×	571 0.9×	951 1.5×	144	11.3k
Bruno O. Villoutreix France	49	3.9k 1.0×	1.4k 0.5×	616 0.3×	451 0.7×	2.3k 3.5×	221	8.5k
A.M. Edwards Canada	65	9.3k 2.5×	1.1k 0.4×	1.6k 0.8×	444 0.7×	469 0.7×	232	13.7k
Alexey A. Lugovskoy United States	31	4.0k 1.0×	1.2k 0.5×	1.2k 0.6×	941 1.4×	199 0.3×	64	5.6k
Marketa Zvelebil United Kingdom	49	5.8k 1.5×	1.1k 0.4×	1.6k 0.8×	246 0.4×	200 0.3×	102	8.2k
Thomas D.Y. Chung United States	24	4.9k 1.3×	608 0.2×	952 0.5×	337 0.5×	542 0.8×	78	7.5k
Sucha Sudarsanam United States	16	6.1k 1.6×	547 0.2×	1.4k 0.7×	380 0.6×	631 1.0×	29	8.1k

Countries citing papers authored by David Gfeller

Since Specialization

Citations

This map shows the geographic impact of David Gfeller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Gfeller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Gfeller more than expected).

Fields of papers citing papers by David Gfeller

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Gfeller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Gfeller. The network helps show where David Gfeller may publish in the future.

Co-authorship network of co-authors of David Gfeller

This figure shows the co-authorship network connecting the top 25 collaborators of David Gfeller. A scholar is included among the top collaborators of David Gfeller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Gfeller. David Gfeller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chevalier, Mathieu F., Vincent Allain, Julien Gras, et al.. (2025). Donor HLA-DQ genetic and functional divergence affect the control of BK polyoma virus infection after kidney transplantation. Science Advances. 11(10). eadt3499–eadt3499. 1 indexed citations

Croce, Giancarlo, Sara Bobisse, Maiia E. Bragina, et al.. (2025). Phage display enables machine learning discovery of cancer antigen–specific TCRs. Science Advances. 11(24). eads5589–eads5589. 2 indexed citations

Andreatta, Massimo, et al.. (2024). Semi-supervised integration of single-cell transcriptomics data. Nature Communications. 15(1). 872–872. 13 indexed citations

Nielsen, Morten, Anne Eugster, Manisha Goel, et al.. (2024). Lessons learned from the IMMREP23 TCR-epitope prediction challenge. SHILAP Revista de lepidopterología. 16. 100045–100045. 14 indexed citations

Tavernari, Daniele, Elena Battistello, Elie Dheilly, et al.. (2021). Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression. Cancer Discovery. 11(6). 1490–1507. 75 indexed citations

Schmidt, Julien, Angela R. Smith, Julien Racle, et al.. (2021). Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. Cell Reports Medicine. 2(2). 100194–100194. 101 indexed citations

Devlin, Jason R., Jesus A. Alonso, Cory M. Ayres, et al.. (2020). Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nature Chemical Biology. 16(11). 1269–1276. 48 indexed citations

Newey, Alice, B Griffiths, Justine Michaux, et al.. (2019). Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment. Journal for ImmunoTherapy of Cancer. 7(1). 309–309. 91 indexed citations

Guillaume, Philippe, Julien Racle, Justine Michaux, et al.. (2019). Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands. Molecular & Cellular Proteomics. 19(2). 390–404. 42 indexed citations

10.

Racle, Julien, Justine Michaux, Marion Arnaud, et al.. (2019). Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nature Biotechnology. 37(11). 1283–1286. 200 indexed citations

11.

Guillaume, Philippe, S. Picaud, Petra Baumgaertner, et al.. (2018). The C-terminal extension landscape of naturally presented HLA-I ligands. Proceedings of the National Academy of Sciences. 115(20). 5083–5088. 35 indexed citations

12.

Ben‐David, Moshe, Haiming Huang, Mark Sun, et al.. (2018). Allosteric Modulation of Binding Specificity by Alternative Packing of Protein Cores. Journal of Molecular Biology. 431(2). 336–350. 16 indexed citations

13.

Beer, Ilan, Christian Iseli, Chloé Chong, et al.. (2018). Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome*. Molecular & Cellular Proteomics. 17(12). 2347–2357. 74 indexed citations

14.

Neubert, Natalie J., Laure Tillé, David Barras, et al.. (2017). Broad and Conserved Immune Regulation by Genetically Heterogeneous Melanoma Cells. Cancer Research. 77(7). 1623–1636. 8 indexed citations

15.

Bassani‐Sternberg, Michal, Chloé Chong, Philippe Guillaume, et al.. (2017). Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Computational Biology. 13(8). e1005725–e1005725. 177 indexed citations

16.

Gfeller, David, Olivier Michielin, & Vincent Zoete. (2012). SwissSidechain: a molecular and structural database of non-natural sidechains. Nucleic Acids Research. 41(D1). D327–D332. 100 indexed citations

17.

Gfeller, David, Olivier Michielin, & Vincent Zoete. (2012). Expanding molecular modeling and design tools to non‐natural sidechains. Journal of Computational Chemistry. 33(18). 1525–1535. 28 indexed citations

18.

Ernst, Andreas, David Gfeller, Zhengyan Kan, et al.. (2010). Coevolution of PDZ domain–ligand interactions analyzed by high-throughput phage display and deep sequencing. Molecular BioSystems. 6(10). 1782–1790. 97 indexed citations

19.

Gfeller, David & Paolo De Los Rios. (2008). Spectral Coarse Graining and Synchronization in Oscillator Networks. Physical Review Letters. 100(17). 174104–174104. 59 indexed citations

20.

Gfeller, David, Jean-Cédric Chappelier, & Paolo De Los Rios. (2005). Synonym Dictionary Improvement through Markov Clustering and Clustering Stability. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 34(6). 106–113. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact