Tom Sercu
- Molecular Biology top 2%
- Machine Learning in Bioinformatics 5
- RNA and protein synthesis mechanisms 3
- Genomics and Phylogenetic Studies 3
- Protein Structure and Dynamics 2
- Microbiology top 5%
- Structural Biology top 10%
- Health Informatics top 10%
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- Speech and Audio Processing 3
- Music and Audio Processing 2
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- Speech Recognition and Synthesis 3
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- Multimodal Machine Learning Applications 3
- Co-authors
- Alexander RivesZeming LinRoshan RaoRobert VerkuilAllan dos Santos CostaMaryam Fazel-ZarandiZhongkai ZhuNikita Smetanin
- Journals
- Nature Biomedical Engineering (1 paper)Science (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesIsraelSingapore
In The Last Decade
Tom Sercu
16 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Molecular Biology 2.9k
- Computational Theory and Mathematics 586
- Microbiology 124
- Structural Biology 21
- Health Informatics 17
Countries citing papers authored by Tom Sercu
This map shows the geographic impact of Tom Sercu'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 Tom Sercu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Sercu more than expected).
Fields of papers citing papers by Tom Sercu
This network shows the impact of papers produced by Tom Sercu. 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 Tom Sercu. The network helps show where Tom Sercu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tom Sercu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Evolutionary-scale prediction of atomic-level protein structure with a language modelbreakdown → | 2023 | 1968 |
| 2 | Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesbreakdown → | 2021 | 1429 |
| 3 | 2021 | 6 | |
| 4 | MSA Transformer | 2021 | 3 |
| 5 | 2021 | 5 | |
| 6 | Transformer protein language models are unsupervised structure learners | 2021 | 2 |
| 7 | Improved Adversarial Image Captioning | 2019 | 1 |
| 8 | Interactive Visual Exploration of Latent Space (IVELS) for Peptide Auto-Encoder Model Selection | 2019 | 3 |
| 9 | 2019 | 3 | |
| 10 | Improved Image Captioning with Adversarial Semantic Alignment. | 2018 | 6 |
| 11 | Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition | 2018 | 15 |
| 12 | Sobolev GAN | 2018 | 6 |
| 13 | Fisher GAN | 2017 | 9 |
| 14 | 2017 | 29 | |
| 15 | 2017 | 16 | |
| 16 | 2016 | 139 |
About Tom Sercu
Tom Sercu is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology and Statistical and Nonlinear Physics, having authored 16 papers that have together received 3.6k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (5 papers), Speech and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers), Multimodal Machine Learning Applications (3 papers), RNA and protein synthesis mechanisms (3 papers), Genomics and Phylogenetic Studies (3 papers), Protein Structure and Dynamics (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Molecular Biology (2.9k citations), Computational Theory and Mathematics (586 citations), Microbiology (124 citations), Structural Biology (21 citations) and Health Informatics (17 citations). Tom Sercu has collaborated with scholars based in United States, Israel and Singapore. Frequent co-authors include Alexander Rives, Zeming Lin, Roshan Rao, Robert Verkuil, Allan dos Santos Costa, Maryam Fazel-Zarandi, Zhongkai Zhu, Nikita Smetanin, Halil Akin and Joshua Meier. Their work appears in journals such as Nature Biomedical Engineering, Science, Proceedings of the National Academy of Sciences, arXiv (Cornell University) and Neural Information Processing Systems.
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.