David Belanger

691 total citations · 1 hit paper
13 papers, 306 citations indexed

About

David Belanger is a scholar working on Molecular Biology, Artificial Intelligence and Oncology. According to data from OpenAlex, David Belanger has authored 13 papers receiving a total of 306 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 6 papers in Artificial Intelligence and 2 papers in Oncology. Recurrent topics in David Belanger's work include Machine Learning in Bioinformatics (4 papers), Machine Learning and Algorithms (3 papers) and Natural Language Processing Techniques (3 papers). David Belanger is often cited by papers focused on Machine Learning in Bioinformatics (4 papers), Machine Learning and Algorithms (3 papers) and Natural Language Processing Techniques (3 papers). David Belanger collaborates with scholars based in United States and United Kingdom. David Belanger's co-authors include Lucy J. Colwell, Maxwell L. Bileschi, Theo Sanderson, Mark A. DePristo, Alex Bateman, Brandon Carter, D. Sculley, Drew Bryant, Gonzalo E. Mena and Scott W. Linderman and has published in prestigious journals such as Nature Biotechnology, eLife and Bioorganic & Medicinal Chemistry Letters.

In The Last Decade

David Belanger

12 papers receiving 294 citations

Hit Papers

Using deep learning to annotate the protein universe 2022 2026 2023 2024 2022 50 100 150
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.

  1. Using deep learning to annotate the protein universe
    2022 164 citations Maxwell L. Bileschi, David Belanger et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Belanger United States 7 219 41 38 23 15 13 306
Yixiao Zhai China 8 268 1.2× 21 0.5× 48 1.3× 24 1.0× 17 1.1× 15 341
René Rahn Germany 5 145 0.7× 67 1.6× 34 0.9× 10 0.4× 21 1.4× 6 235
Ben Krause United States 5 357 1.6× 68 1.7× 62 1.6× 46 2.0× 12 0.8× 22 547
Xiaoqi Wang China 11 171 0.8× 33 0.8× 88 2.3× 29 1.3× 13 0.9× 23 257
Sabrina de Azevedo Silveira Brazil 9 207 0.9× 14 0.3× 84 2.2× 26 1.1× 13 0.9× 24 319
Guangmin Liang China 8 301 1.4× 33 0.8× 65 1.7× 21 0.9× 9 0.6× 14 395
Jeffrey M. Schaub United States 10 383 1.7× 37 0.9× 25 0.7× 12 0.5× 9 0.6× 16 468
Henry Soldano France 9 116 0.5× 63 1.5× 38 1.0× 22 1.0× 6 0.4× 31 203
Luca Pireddu Italy 10 250 1.1× 76 1.9× 44 1.2× 7 0.3× 11 0.7× 28 387

Countries citing papers authored by David Belanger

Since Specialization
Citations

This map shows the geographic impact of David Belanger'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 Belanger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Belanger more than expected).

Fields of papers citing papers by David Belanger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Belanger. 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 Belanger. The network helps show where David Belanger may publish in the future.

Co-authorship network of co-authors of David Belanger

This figure shows the co-authorship network connecting the top 25 collaborators of David Belanger. A scholar is included among the top collaborators of David Belanger 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 Belanger. David Belanger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Thomas, Neil, David Belanger, Chenling Xu, et al.. (2025). Engineering highly active nuclease enzymes with machine learning and high-throughput screening. Cell Systems. 16(3). 101236–101236. 6 indexed citations
2.
Sanderson, Theo, Maxwell L. Bileschi, David Belanger, & Lucy J. Colwell. (2023). ProteInfer, deep neural networks for protein functional inference. eLife. 12. 82 indexed citations
3.
Belanger, David & Lucy J. Colwell. (2023). Hallucinating functional protein sequences. Nature Biotechnology. 41(8). 1073–1074. 1 indexed citations
4.
Bileschi, Maxwell L., David Belanger, Drew Bryant, et al.. (2022). Using deep learning to annotate the protein universe. Nature Biotechnology. 40(6). 932–937. 164 indexed citations breakdown →
5.
6.
Chao, Sherry & David Belanger. (2021). Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types. 3375–3385. 5 indexed citations
7.
Mena, Gonzalo E., David Belanger, Scott W. Linderman, & Jasper Snoek. (2018). Learning Latent Permutations with Gumbel-Sinkhorn Networks. Oxford University Research Archive (ORA) (University of Oxford). 19 indexed citations
8.
Belanger, David, et al.. (2016). Bethe Learning of Graphical Models via MAP Decoding. International Conference on Artificial Intelligence and Statistics. 1096–1104. 1 indexed citations
9.
Singh, Sameer, Limin Yao, David Belanger, et al.. (2013). Universal Schema for Slot Filling and Cold Start: UMass IESL at TACKBP 2013.. Theory and applications of categories. 3 indexed citations
10.
Belanger, David, Daniel Sheldon, & Andrew McCallum. (2013). Marginal Inference in MRFs using Frank-Wolfe. 6 indexed citations
11.
Belanger, David, Alexandre Passos, Sebastian Riedel, & Andrew McCallum. (2012). MAP Inference in Chains using Column Generation. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 25. 1844–1852. 3 indexed citations
12.
Voss, Matthew E., Matthew P. Rainka, Mike Fleming, et al.. (2012). Synthesis and SAR studies of imidazo-[1,2-a]-pyrazine Aurora kinase inhibitors with improved off-target kinase selectivity. Bioorganic & Medicinal Chemistry Letters. 22(10). 3544–3549. 9 indexed citations
13.
Belanger, David. (2004). SableJIT: A retargetable just-in-time compiler. eScholarship@McGill (McGill). 1 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.

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