Eugene W. Myers

160.7k total citations · 8 hit papers
126 papers, 88.6k citations indexed

About

Eugene W. Myers is a scholar working on Molecular Biology, Artificial Intelligence and Biophysics. According to data from OpenAlex, Eugene W. Myers has authored 126 papers receiving a total of 88.6k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Molecular Biology, 32 papers in Artificial Intelligence and 28 papers in Biophysics. Recurrent topics in Eugene W. Myers's work include Genomics and Phylogenetic Studies (42 papers), Algorithms and Data Compression (27 papers) and Cell Image Analysis Techniques (26 papers). Eugene W. Myers is often cited by papers focused on Genomics and Phylogenetic Studies (42 papers), Algorithms and Data Compression (27 papers) and Cell Image Analysis Techniques (26 papers). Eugene W. Myers collaborates with scholars based in United States, Germany and United Kingdom. Eugene W. Myers's co-authors include Webb Miller, Stephen F. Altschul, Warren Gish, David J. Lipman, Webb Miller, R. C. Edgar, Fuhui Long, Anthony A. Hyman, Löıc A. Royer and Martin Weigert and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Eugene W. Myers

121 papers receiving 86.3k citations

Hit Papers

Basic local alignment sea... 1986 2026 1999 2012 1990 2015 1988 2002 1986 25.0k 50.0k 75.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Eugene W. Myers United States 49 50.9k 19.4k 14.0k 11.8k 5.6k 126 88.6k
Warren Gish United States 17 46.9k 0.9× 18.4k 0.9× 13.8k 1.0× 10.5k 0.9× 5.0k 0.9× 20 80.8k
Webb Miller United States 10 43.9k 0.9× 17.6k 0.9× 13.5k 1.0× 9.9k 0.8× 4.8k 0.9× 10 76.7k
David J. Lipman United States 50 66.4k 1.3× 22.8k 1.2× 18.0k 1.3× 15.0k 1.3× 6.5k 1.2× 80 112.8k
Naruya Saitou Japan 45 30.6k 0.6× 14.6k 0.8× 12.8k 0.9× 10.8k 0.9× 4.1k 0.7× 199 58.9k
R. C. Edgar United States 32 41.7k 0.8× 21.7k 1.1× 25.3k 1.8× 8.4k 0.7× 5.5k 1.0× 50 89.7k
Toby J. Gibson Germany 69 65.8k 1.3× 24.7k 1.3× 16.2k 1.2× 16.2k 1.4× 8.7k 1.5× 148 116.5k
Sean R. Eddy United States 80 54.3k 1.1× 18.3k 0.9× 13.1k 0.9× 9.1k 0.8× 2.8k 0.5× 127 75.9k
Minoru Kanehisa Japan 75 76.3k 1.5× 14.1k 0.7× 11.1k 0.8× 10.1k 0.9× 3.7k 0.7× 266 114.1k
Prescott L. Deininger United States 67 62.5k 1.2× 22.6k 1.2× 8.7k 0.6× 20.0k 1.7× 6.1k 1.1× 197 98.6k
Rodrigo López United Kingdom 32 32.5k 0.6× 13.2k 0.7× 7.9k 0.6× 7.3k 0.6× 3.6k 0.6× 68 57.4k

Countries citing papers authored by Eugene W. Myers

Since Specialization
Citations

This map shows the geographic impact of Eugene W. Myers'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 Eugene W. Myers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene W. Myers more than expected).

Fields of papers citing papers by Eugene W. Myers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eugene W. Myers. 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 Eugene W. Myers. The network helps show where Eugene W. Myers may publish in the future.

Co-authorship network of co-authors of Eugene W. Myers

This figure shows the co-authorship network connecting the top 25 collaborators of Eugene W. Myers. A scholar is included among the top collaborators of Eugene W. Myers based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eugene W. Myers. Eugene W. Myers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Formenti, Giulio, Bonhwang Koo, Jennifer Balacco, et al.. (2025). Evaluation of sequencing reads at scale using rdeval. Bioinformatics. 41(9).
4.
Puechmaille, Sébastien J., Sarahjane Power, Martin Pippel, et al.. (2023). Comparative Genome Microsynteny Illuminates the Fast Evolution of Nuclear Mitochondrial Segments (NUMTs) in Mammals. Molecular Biology and Evolution. 41(1). 11 indexed citations
5.
Shatilovich, Anastasia, Martin Pippel, Alexei V. Tchesunov, et al.. (2023). A novel nematode species from the Siberian permafrost shares adaptive mechanisms for cryptobiotic survival with C. elegans dauer larva. PLoS Genetics. 19(7). e1010798–e1010798. 17 indexed citations
6.
Pippel, Martin, et al.. (2022). DENTIST—using long reads for closing assembly gaps at high accuracy. GigaScience. 11. 13 indexed citations
7.
Doronina, Liliya, Graham M. Hughes, Diana D. Moreno-Santillán, et al.. (2022). Contradictory Phylogenetic Signals in the Laurasiatheria Anomaly Zone. Genes. 13(5). 766–766. 8 indexed citations
8.
Wang, Yuhan, Mark Eddison, Greg Fleishman, et al.. (2021). EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization. Cell. 184(26). 6361–6377.e24. 91 indexed citations
9.
Moreno-Santillán, Diana D., Tanya M. Lama, Huabin Zhao, et al.. (2021). Large‐scale genome sampling reveals unique immunity and metabolic adaptations in bats. Molecular Ecology. 30(23). 6449–6467. 55 indexed citations
10.
Kautt, Andreas F., Claudius F. Kratochwil, Alexander Nater, et al.. (2020). Contrasting signatures of genomic divergence during sympatric speciation. Nature. 588(7836). 106–111. 118 indexed citations
11.
Teeling, Emma C., Sonja C. Vernes, Liliana M. Dávalos, et al.. (2017). Bat Biology, Genomes, and the Bat1K Project: To Generate Chromosome-Level Genomes for All Living Bat Species. Annual Review of Animal Biosciences. 6(1). 23–46. 140 indexed citations
12.
Dye, Natalie A., Marko Popović, Stephanie Spannl, et al.. (2017). Cell dynamics underlying oriented growth of the Drosophila wing imaginal disc. Development. 144(23). 4406–4421. 54 indexed citations
13.
Amat, Fernando, William C. Lemon, Daniel P. Mossing, et al.. (2014). Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature Methods. 11(9). 951–958. 196 indexed citations
14.
Preibisch, Stephan, Fernando Amat, Evangelia Stamataki, et al.. (2013). Efficient bayesian multi-view deconvolution. arXiv (Cornell University). 1 indexed citations
15.
Clack, Nathan, Daniel H. O’Connor, Daniel Huber, et al.. (2012). Automated Tracking of Whiskers in Videos of Head Fixed Rodents. PLoS Computational Biology. 8(7). e1002591–e1002591. 104 indexed citations
16.
Mwangi, Michael, Shang Wei Wu, Yanjiao Zhou, et al.. (2007). Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing. Proceedings of the National Academy of Sciences. 104(22). 9451–9456. 426 indexed citations
17.
Begun, David J, Alisha K. Holloway, Kristian Stevens, et al.. (2007). Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans. PLoS Biology. 5(11). e310–e310. 514 indexed citations breakdown →
18.
Jain, Mayank & Eugene W. Myers. (1997). Algorithms for Computing and Integrating Physical Maps Using Unique Probes. Journal of Computational Biology. 4(4). 449–466. 6 indexed citations
19.
Myers, Eugene W., et al.. (1982). D-CRACKING: PAVEMENT DESIGN AND CONSTRUCTION VARIABLES. Transportation Research Record Journal of the Transportation Research Board. 2 indexed citations
20.
Myers, Eugene W. & Leon J. Osterweil. (1981). BIGMAC II: A FORTRAN language augmentation tool. International Conference on Software Engineering. 410–421. 2 indexed citations

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.

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