Tom Gibbs
Impact in
- Molecular Biology top 10%
- Machine Learning in Bioinformatics
- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- vaccines and immunoinformatics approaches
- Bioinformatics and Genomic Networks
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- Computational Drug Discovery Methods
Papers in
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- Natural Language Processing Techniques 1
- Co-authors
- Christian Dallago (1 shared paper)Ghalia Rehawi (1 shared paper)Llion Jones (1 shared paper)Christoph Angerer (1 shared paper)Martin Steinegger (1 shared paper)Yu Wang (1 shared paper)Debsindhu Bhowmik (1 shared paper)Burkhard Rost (1 shared paper)
- Journals
- Journal of Environmental Science and Health Part A (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) (1 paper)
- Partner nations
- United StatesGermanySingapore
In The Last Decade
Tom Gibbs
4 papers receiving 1.2k citations
Tom Gibbs's Hit Papers
Peers
Comparison fields: 5 of 82
- Molecular Biology 913
- Computational Theory and Mathematics 186
- Microbiology 65
- Health Informatics 6
- Radiology, Nuclear Medicine and Imaging 68
Countries citing papers authored by Tom Gibbs
This map shows the geographic impact of Tom Gibbs'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 Gibbs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Gibbs more than expected).
Fields of papers citing papers by Tom Gibbs
This network shows the impact of papers produced by Tom Gibbs. 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 Gibbs. The network helps show where Tom Gibbs may publish in the future.
Co-authors
The 25 scholars most cited alongside Tom Gibbs, 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 | ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning Hit paper breakdown → | 2021 | 1192 |
| 2 | 1999 | 8 | |
| 3 | 2025 | 5 | |
| 4 | 2022 | 1 |
About Tom Gibbs
Tom Gibbs is a scholar working on Artificial Intelligence, Endocrinology, Food Science, Developmental and Educational Psychology and Nuclear and High Energy Physics, having authored 4 papers that have together received 1.2k indexed citations. Recurring topics across this work include Biosensors and Analytical Detection (1 paper), Dark Matter and Cosmic Phenomena (1 paper), Manufacturing Process and Optimization (1 paper), Neutrino Physics Research (1 paper), Natural Language Processing Techniques (1 paper), Machine Learning in Materials Science (1 paper), Innovative Teaching and Learning Methods (1 paper) and Digital Transformation in Industry (1 paper). The work is most often cited by research in Molecular Biology (913 citations), Computational Theory and Mathematics (186 citations), Microbiology (65 citations), Health Informatics (6 citations) and Radiology, Nuclear Medicine and Imaging (68 citations). Tom Gibbs has collaborated with scholars based in United States, Germany and Singapore. Frequent co-authors include Christian Dallago, Ghalia Rehawi, Llion Jones, Christoph Angerer, Martin Steinegger, Yu Wang, Debsindhu Bhowmik, Burkhard Rost, Ahmed Elnaggar and T. Fehér. Their work appears in journals such as Journal of Environmental Science and Health Part A, IEEE Transactions on Pattern Analysis and Machine Intelligence and DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron).
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.