Tal Pupko
- Molecular Biology top 0.2%
- Genomics and Phylogenetic Studies 73
- RNA and protein synthesis mechanisms 36
- Protein Structure and Dynamics 17
- Machine Learning in Bioinformatics 15
- Endocrinology top 0.5%
- Genetics top 0.5%
- Genetic diversity and population structure 24
- Cell Biology top 1%
- Ecology top 1%
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- Evolution and Paleontology Studies 13
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- Plant Pathogenic Bacteria Studies 12
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- Monoclonal and Polyclonal Antibodies Research 12
- Co-authors
- Nir Ben‐TalEric MartzHaim AshkenazyItay MayroseFabian GlaserShiran AbadiElona ErezDan Graur
- Journals
- Bioinformatics (19 papers)Molecular Biology and Evolution (17 papers)Nucleic Acids Research (16 papers)
- Partner nations
- IsraelUnited StatesJapan
In The Last Decade
Tal Pupko
140 papers receiving 14.9k citations
Hit Papers
Peers
Comparison fields: 5 of 185
- Molecular Biology 10.7k
- Endocrinology 784
- Genetics 2.7k
- Cell Biology 1.0k
- Ecology 1.5k
Countries citing papers authored by Tal Pupko
This map shows the geographic impact of Tal Pupko'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 Tal Pupko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tal Pupko more than expected).
Fields of papers citing papers by Tal Pupko
This network shows the impact of papers produced by Tal Pupko. 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 Tal Pupko. The network helps show where Tal Pupko may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tal Pupko, 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 | 2024 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 9 | |
| 4 | 2023 | 13 | |
| 5 | 2023 | 4 | |
| 6 | Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurfbreakdown → | 2023 | 186 |
| 7 | 2022 | 5 | |
| 8 | 2022 | 4 | |
| 9 | 2021 | 16 | |
| 10 | 2021 | 17 | |
| 11 | 2021 | 70 | |
| 12 | 2020 | 31 | |
| 13 | 2020 | 31 | |
| 14 | 2019 | 98 | |
| 15 | ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromoleculesbreakdown → | 2016 | 2170 |
| 16 | 2016 | 24 | |
| 17 | 2015 | 53 | |
| 18 | ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acidsbreakdown → | 2010 | 1494 |
| 19 | 2009 | 177 | |
| 20 | 2007 | 47 |
About Tal Pupko
Tal Pupko is a scholar working on Paleontology, Molecular Biology and Genetics, having authored 142 papers that have together received 15.1k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (73 papers), RNA and protein synthesis mechanisms (36 papers), Genetic diversity and population structure (24 papers), Protein Structure and Dynamics (17 papers), Machine Learning in Bioinformatics (15 papers), Evolution and Paleontology Studies (13 papers), Plant Pathogenic Bacteria Studies (12 papers) and Monoclonal and Polyclonal Antibodies Research (12 papers). The work is most often cited by research in Molecular Biology (10.7k citations), Endocrinology (784 citations) and Genetics (2.7k citations). Tal Pupko has collaborated with scholars based in Israel, United States and Japan. Frequent co-authors include Nir Ben‐Tal, Eric Martz, Haim Ashkenazy, Itay Mayrose, Fabian Glaser, Shiran Abadi, Elona Erez, Dan Graur, Inbal Paz and Ofir Cohen. Their work appears in journals such as Bioinformatics, Molecular Biology and Evolution, Nucleic Acids Research, Genome Biology and Evolution and Proceedings of the National Academy of Sciences.
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.