David A. Liberles

6.9k total citations
107 papers, 3.3k citations indexed

About

David A. Liberles is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, David A. Liberles has authored 107 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Molecular Biology, 45 papers in Genetics and 15 papers in Plant Science. Recurrent topics in David A. Liberles's work include Genomics and Phylogenetic Studies (52 papers), Evolution and Genetic Dynamics (24 papers) and RNA and protein synthesis mechanisms (18 papers). David A. Liberles is often cited by papers focused on Genomics and Phylogenetic Studies (52 papers), Evolution and Genetic Dynamics (24 papers) and RNA and protein synthesis mechanisms (18 papers). David A. Liberles collaborates with scholars based in United States, Norway and Sweden. David A. Liberles's co-authors include Shruti Rastogi, Christian Tellgren‐Roth, Timothy R. Hughes, Katharina Dittmar, Johan A. Grahnen, Jean‐Benoît Peltier, Peter Roepstorff, A. Jimmy Ytterberg, Maria Anisimova and Steven A. Benner and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

David A. Liberles

104 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David A. Liberles United States 31 2.5k 970 669 214 213 107 3.3k
Karl Nordström Sweden 35 3.1k 1.2× 1.4k 1.4× 799 1.2× 310 1.4× 638 3.0× 82 4.4k
Tim Schedl United States 48 4.3k 1.8× 734 0.8× 585 0.9× 187 0.9× 183 0.9× 95 7.4k
Anish Kejariwal United States 7 2.4k 1.0× 1.0k 1.0× 299 0.4× 154 0.7× 106 0.5× 7 3.9k
Xun Gu United States 34 3.1k 1.3× 1.2k 1.3× 1.2k 1.8× 170 0.8× 347 1.6× 106 4.8k
Christian Cole United Kingdom 20 3.8k 1.5× 585 0.6× 524 0.8× 186 0.9× 318 1.5× 49 5.3k
Yitzhak Pilpel Israel 49 6.7k 2.7× 1.3k 1.3× 670 1.0× 705 3.3× 302 1.4× 100 8.3k
David Gresham United States 32 3.0k 1.2× 1.5k 1.5× 655 1.0× 197 0.9× 180 0.8× 79 4.4k
M. Mar Albà Spain 39 4.0k 1.6× 850 0.9× 942 1.4× 260 1.2× 248 1.2× 80 5.7k
Donald G. Moerman Canada 44 3.9k 1.6× 944 1.0× 649 1.0× 394 1.8× 181 0.8× 100 6.0k
Kim C. Worley United States 29 2.5k 1.0× 1.8k 1.9× 620 0.9× 259 1.2× 403 1.9× 72 4.1k

Countries citing papers authored by David A. Liberles

Since Specialization
Citations

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

Fields of papers citing papers by David A. Liberles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David A. Liberles. 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 David A. Liberles. The network helps show where David A. Liberles may publish in the future.

Co-authorship network of co-authors of David A. Liberles

This figure shows the co-authorship network connecting the top 25 collaborators of David A. Liberles. A scholar is included among the top collaborators of David A. Liberles 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 David A. Liberles. David A. Liberles 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
1.
Liberles, David A., et al.. (2025). Understanding Functional Evolution in Orthologs and Paralogs. Journal of Molecular Evolution. 93(6). 730–739.
2.
Ritchie, Andrew M., et al.. (2023). Highly Abundant Proteins Are Highly Thermostable. Genome Biology and Evolution. 15(7). 1 indexed citations
3.
Henry, Camille, et al.. (2022). WGDTree: a phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events. BMC Bioinformatics. 23(1). 505–505. 2 indexed citations
4.
Shank, Stephen D., et al.. (2020). Characterizing lineage-specific evolution and the processes driving genomic diversification in chordates. BMC Evolutionary Biology. 20(1). 24–24. 1 indexed citations
5.
Platt, Alexander, Claudia Weber, & David A. Liberles. (2018). Protein evolution depends on multiple distinct population size parameters. BMC Evolutionary Biology. 18(1). 17–17. 10 indexed citations
6.
Fragata, Inês, et al.. (2018). Evolution in the light of fitness landscape theory. Trends in Ecology & Evolution. 34(1). 69–82. 97 indexed citations
7.
Hermansen, Russell A., et al.. (2017). The Adaptive Evolution Database (TAED): A New Release of a Database of Phylogenetically Indexed Gene Families from Chordates. Journal of Molecular Evolution. 85(1-2). 46–56. 4 indexed citations
8.
Kamneva, Olga K., et al.. (2012). Analysis of Genome Content Evolution in PVC Bacterial Super-Phylum: Assessment of Candidate Genes Associated with Cellular Organization and Lifestyle. Genome Biology and Evolution. 4(12). 1375–1390. 41 indexed citations
9.
Lai, Jason, et al.. (2012). A Phylogenetic Analysis of Normal Modes Evolution in Enzymes and Its Relationship to Enzyme Function. Journal of Molecular Biology. 422(3). 442–459. 20 indexed citations
10.
Konrad, Anke, et al.. (2011). The global distribution and evolution of deoxyribonucleoside kinases in bacteria. Gene. 492(1). 117–120. 5 indexed citations
11.
Dittmar, Katharina & David A. Liberles. (2010). Evolution after gene duplication. Wiley-Blackwell eBooks. 53 indexed citations
12.
Huzurbazar, Snehalata, et al.. (2009). Lineage-Specific Differences in the Amino Acid Substitution Process. Journal of Molecular Biology. 396(5). 1410–1421. 9 indexed citations
13.
Hughes, Timothy R. & David A. Liberles. (2008). Whole-Genome Duplications in the Ancestral Vertebrate Are Detectable in the Distribution of Gene Family Sizes of Tetrapod Species. Journal of Molecular Evolution. 67(4). 343–357. 27 indexed citations
14.
Kolesov, Grigory, et al.. (2008). Complex Microsatellite Dynamics in the Myostatin Gene Within Ruminants. Journal of Molecular Evolution. 66(3). 258–265. 1 indexed citations
15.
Hughes, Timothy R. & David A. Liberles. (2008). The power-law distribution of gene family size is driven by the pseudogenisation rate's heterogeneity between gene families. Gene. 414(1-2). 85–94. 18 indexed citations
16.
Hughes, Timothy R., et al.. (2007). Evaluating dosage compensation as a cause of duplicate gene retention in Paramecium tetraurelia. Genome Biology. 8(5). 213–213. 39 indexed citations
17.
Skovgaard, Marie, János T. Kodra, Dorte X. Gram, et al.. (2006). Using Evolutionary Information and Ancestral Sequences to Understand the Sequence–Function Relationship in GLP-1 Agonists. Journal of Molecular Biology. 363(5). 977–988. 19 indexed citations
18.
Eidhammer, Ingvar, et al.. (2005). Phylogenetic reconstruction of ancestral character states for gene expression and mRNA splicing data. BMC Bioinformatics. 6(1). 127–127. 9 indexed citations
19.
Zhao, Yaofeng, Imre Kacskovıcs, Qiang Pan‐Hammarström, et al.. (2002). Artiodactyl IgD: The Missing Link. The Journal of Immunology. 169(8). 4408–4416. 81 indexed citations
20.
Liberles, David A.. (2001). Evaluation of Methods for Determination of a Reconstructed History of Gene Sequence Evolution. Molecular Biology and Evolution. 18(11). 2040–2047. 54 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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