Michael Suleski
Impact in
- Paleontology top 5%
- Evolution and Paleontology Studies
- Genetics top 2%
- Genetic diversity and population structure
Papers in
-
- Genomics and Phylogenetic Studies 6
- Machine Learning in Bioinformatics 1
- Genetics 4
- Genetic diversity and population structure 3
- Genomics and Rare Diseases 1
- Co-authors
- Sudhir Kumar (7 shared papers)S. Blair Hedges (3 shared papers)Glen Stecher (5 shared papers)Julie Marin (1 shared paper)Maxwell Sanderford (3 shared papers)Jack M. Craig (1 shared paper)Sudip Sharma (2 shared papers)Koichiro Tamura (2 shared papers)
- Journals
- Molecular Biology and Evolution (5 papers)Journal of Molecular Evolution (1 paper)Genome Research (1 paper)
- Partner nations
- United StatesJapanSaudi Arabia
In The Last Decade
Michael Suleski
5 papers receiving 3.4k citations
Michael Suleski's Hit Papers
Peers
Comparison fields: 5 of 137
- Paleontology 265
- Genetics 830
- Ecology, Evolution, Behavior and Systematics 529
- Ecological Modeling 112
- Molecular Biology 1.8k
Countries citing papers authored by Michael Suleski
This map shows the geographic impact of Michael Suleski'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 Michael Suleski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Suleski more than expected).
Fields of papers citing papers by Michael Suleski
This network shows the impact of papers produced by Michael Suleski. 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 Michael Suleski. The network helps show where Michael Suleski may publish in the future.
Co-authors
The 12 scholars most cited alongside Michael Suleski, 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 | TimeTree: A Resource for Timelines, Timetrees, and Divergence Times Hit paper breakdown → | 2017 | 1847 |
| 2 | Tree of Life Reveals Clock-Like Speciation and Diversification Hit paper breakdown → | 2015 | 706 |
| 3 | TimeTree 5: An Expanded Resource for Species Divergence Times Hit paper breakdown → | 2022 | 656 |
| 4 | MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing Hit paper breakdown → | 2024 | 196 |
| 5 | 2009 | 48 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 0 |
About Michael Suleski
Michael Suleski is a scholar working on Molecular Biology, Genetics, Ecological Modeling, Paleontology and Ecology, having authored 7 papers that have together received 3.5k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (6 papers), Genetic diversity and population structure (3 papers), Species Distribution and Climate Change (2 papers), Machine Learning in Bioinformatics (1 paper), Genomics and Rare Diseases (1 paper), Evolutionary Algorithms and Applications (1 paper), Environmental DNA in Biodiversity Studies (1 paper) and Evolution and Paleontology Studies (1 paper). The work is most often cited by research in Paleontology (265 citations), Genetics (830 citations), Ecology, Evolution, Behavior and Systematics (529 citations), Ecological Modeling (112 citations) and Molecular Biology (1.8k citations). Michael Suleski has collaborated with scholars based in United States, Japan and Saudi Arabia. Frequent co-authors include Sudhir Kumar, S. Blair Hedges, Glen Stecher, Julie Marin, Maxwell Sanderford, Jack M. Craig, Sudip Sharma, Koichiro Tamura, Glenn J. Markov and Antonio De Marco. Their work appears in journals such as Molecular Biology and Evolution, Journal of Molecular Evolution and Genome Research.
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.