Sudhir Kumar
Impact in
- Plant Science top 0.01%
- Plant-Microbe Interactions and Immunity
- Plant Virus Research Studies
- Plant Molecular Biology Research
- Ecology top 0.01%
- Microbial Community Ecology and Physiology
Papers in
-
- Genomics and Phylogenetic Studies 110
- RNA and protein synthesis mechanisms 24
- Gene expression and cancer classification 12
- Genetics 97
- Genetic diversity and population structure 53
- Evolution and Genetic Dynamics 34
- Co-authors
- Koichiro Tamura (34 shared papers)Glen Stecher (14 shared papers)M Nei (5 shared papers)Alan Filipski (12 shared papers)Daniel S. Peterson (4 shared papers)Joel T. Dudley (11 shared papers)Masatoshi Nei (7 shared papers)Daniel G. Peterson (1 shared paper)
- Journals
- Molecular Biology and Evolution (64 papers)Bioinformatics (22 papers)Genetics (8 papers)Proceedings of the National Academy of Sciences (6 papers)BMC Bioinformatics (6 papers)
- Partner nations
- United StatesSaudi ArabiaJapan
In The Last Decade
Sudhir Kumar
263 papers receiving 222.6k citations
Sudhir Kumar's Hit Papers
Peers
Comparison fields: 5 of 218
- Plant Science 62.9k
- Ecology 42.6k
- Parasitology 10.2k
- Endocrinology 7.7k
- Horticulture 1.5k
Countries citing papers authored by Sudhir Kumar
This map shows the geographic impact of Sudhir Kumar'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 Sudhir Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sudhir Kumar more than expected).
Fields of papers citing papers by Sudhir Kumar
This network shows the impact of papers produced by Sudhir Kumar. 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 Sudhir Kumar. The network helps show where Sudhir Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside Sudhir Kumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 275 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0 Hit paper breakdown → | 2013 | 40988 |
| 2 | MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods Hit paper breakdown → | 2011 | 35572 |
| 3 | MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets Hit paper breakdown → | 2016 | 35411 |
| 4 | MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms Hit paper breakdown → | 2018 | 27767 |
| 5 | MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0 Hit paper breakdown → | 2007 | 25953 |
| 6 | MEGA11: Molecular Evolutionary Genetics Analysis Version 11 Hit paper breakdown → | 2021 | 12709 |
| 7 | MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment Hit paper breakdown → | 2004 | 10745 |
| 8 | Molecular Evolution and Phylogenetics Hit paper breakdown → | 2000 | 6310 |
| 9 | MEGA2: molecular evolutionary genetics analysis software Hit paper breakdown → | 2001 | 5669 |
| 10 | Prospects for inferring very large phylogenies by using the neighbor-joining method Hit paper breakdown → | 2004 | 4395 |
| 11 | MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences Hit paper breakdown → | 2008 | 2999 |
| 12 | TimeTree: A Resource for Timelines, Timetrees, and Divergence Times Hit paper breakdown → | 2017 | 1901 |
| 13 | A molecular timescale for vertebrate evolution Hit paper breakdown → | 1998 | 1547 |
| 14 | Molecular Evolutionary Genetics Analysis (MEGA) for macOS Hit paper breakdown → | 2019 | 1188 |
| 15 | MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers Hit paper breakdown → | 1994 | 994 |
| 16 | TimeTree: a public knowledge-base of divergence times among organisms Hit paper breakdown → | 2006 | 915 |
| 17 | TimeTree 5: An Expanded Resource for Species Divergence Times Hit paper breakdown → | 2022 | 732 |
| 18 | Tree of Life Reveals Clock-Like Speciation and Diversification Hit paper breakdown → | 2015 | 721 |
| 19 | A new method of inference of ancestral nucleotide and amino acid sequences. Hit paper breakdown → | 1995 | 554 |
| 20 | Estimating divergence times in large molecular phylogenies Hit paper breakdown → | 2012 | 472 |
About Sudhir Kumar
Sudhir Kumar is a scholar working on Molecular Biology, Genetics, Paleontology, Plant Science and Cancer Research, having authored 275 papers that have together received 227.7k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (110 papers), Genetic diversity and population structure (53 papers), Evolution and Genetic Dynamics (34 papers), RNA and protein synthesis mechanisms (24 papers), Evolution and Paleontology Studies (23 papers), Cancer Genomics and Diagnostics (18 papers), Chromosomal and Genetic Variations (14 papers) and Gene expression and cancer classification (12 papers). The work is most often cited by research in Plant Science (62.9k citations), Ecology (42.6k citations), Parasitology (10.2k citations), Endocrinology (7.7k citations) and Horticulture (1.5k citations). Sudhir Kumar has collaborated with scholars based in United States, Saudi Arabia and Japan. Frequent co-authors include Koichiro Tamura, Glen Stecher, M Nei, Alan Filipski, Daniel S. Peterson, Joel T. Dudley, Masatoshi Nei, Daniel G. Peterson, Michael Li and S. Blair Hedges. Their work appears in journals such as Molecular Biology and Evolution, Bioinformatics, Genetics, Proceedings of the National Academy of Sciences and BMC Bioinformatics.
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.