Gerald J. Wyckoff

3.4k total citations
45 papers, 2.4k citations indexed

About

Gerald J. Wyckoff is a scholar working on Molecular Biology, Genetics and Computational Theory and Mathematics. According to data from OpenAlex, Gerald J. Wyckoff has authored 45 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 11 papers in Genetics and 7 papers in Computational Theory and Mathematics. Recurrent topics in Gerald J. Wyckoff's work include Computational Drug Discovery Methods (7 papers), Genetic diversity and population structure (7 papers) and Genomics and Phylogenetic Studies (6 papers). Gerald J. Wyckoff is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Genetic diversity and population structure (7 papers) and Genomics and Phylogenetic Studies (6 papers). Gerald J. Wyckoff collaborates with scholars based in United States, China and South Africa. Gerald J. Wyckoff's co-authors include Chung‐I Wu, Justin C. Fay, Wen Wang, Bruce T. Lahn, Steve Dorus, Patrick Evans, Christine M. Malcom, Eric J. Vallender, Sun Shim Choi and Sandra L. Gilbert and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Gerald J. Wyckoff

41 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gerald J. Wyckoff United States 19 1.3k 1.2k 331 283 150 45 2.4k
Frank W. Albert United States 22 1.7k 1.4× 1.4k 1.2× 291 0.9× 166 0.6× 180 1.2× 44 2.9k
Steve Dorus United States 26 1.0k 0.8× 1.2k 1.0× 248 0.7× 556 2.0× 173 1.2× 55 2.5k
Eric S. Haag United States 24 838 0.7× 1.0k 0.9× 427 1.3× 313 1.1× 262 1.7× 57 2.0k
Jiřı́ Forejt Czechia 32 1.6k 1.3× 2.0k 1.7× 814 2.5× 225 0.8× 183 1.2× 94 3.1k
Wenming Zhao China 28 1.1k 0.9× 580 0.5× 436 1.3× 81 0.3× 135 0.9× 158 2.7k
David Brawand Switzerland 8 1.2k 1.0× 944 0.8× 334 1.0× 207 0.7× 112 0.7× 10 2.2k
Kim C. Worley United States 29 2.5k 2.0× 1.8k 1.5× 620 1.9× 325 1.1× 403 2.7× 72 4.1k
Anna J. Jasinska United States 25 1.1k 0.9× 551 0.5× 176 0.5× 87 0.3× 56 0.4× 61 2.0k
Adi Fledel-Alon United States 9 1.6k 1.3× 1.7k 1.4× 763 2.3× 145 0.5× 83 0.6× 9 2.9k
Richard D. Emes United Kingdom 35 1.9k 1.5× 786 0.7× 236 0.7× 177 0.6× 223 1.5× 125 4.0k

Countries citing papers authored by Gerald J. Wyckoff

Since Specialization
Citations

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

Fields of papers citing papers by Gerald J. Wyckoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gerald J. Wyckoff. 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 Gerald J. Wyckoff. The network helps show where Gerald J. Wyckoff may publish in the future.

Co-authorship network of co-authors of Gerald J. Wyckoff

This figure shows the co-authorship network connecting the top 25 collaborators of Gerald J. Wyckoff. A scholar is included among the top collaborators of Gerald J. Wyckoff 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 Gerald J. Wyckoff. Gerald J. Wyckoff 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.
Spector, Benjamin L., Warren Cheung, Reid S. Alisch, et al.. (2025). Total plasma cfDNA methylation in pediatric kidney transplant recipients provides insight into acute allograft rejection pathophysiology. Clinical Immunology. 275. 110475–110475.
2.
Xu, Xuan, Shahzad Raza, Jim E. Riviere, et al.. (2023). Identification of genes encoding targets associated with adverse events in multiple myeloma.. Journal of Clinical Oncology. 41(16_suppl). 1556–1556. 2 indexed citations
3.
4.
Xu, Xuan, Majid Jaberi‐Douraki, Faiz Anwer, et al.. (2023). A novel risk assessment metric for antimyeloma therapies and drug interactions.. Journal of Clinical Oncology. 41(16_suppl). e24082–e24082. 2 indexed citations
5.
6.
Jaberi‐Douraki, Majid, et al.. (2021). Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity. Scientific Reports. 11(1). 13349–13349. 5 indexed citations
7.
Xu, Xuan, Reza Mazloom, Joshua M. Staley, et al.. (2019). Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats. Topics in companion animal medicine. 37. 100366–100366. 15 indexed citations
8.
Heruth, Daniel P., Weibin Wu, Ding‐You Li, et al.. (2019). Identification of Novel Regulatory Genes in APAP Induced Hepatocyte Toxicity by a Genome-Wide CRISPR-Cas9 Screen. Scientific Reports. 9(1). 1396–1396. 11 indexed citations
9.
Xu, Shaohua, Ziwen He, Zixiao Guo, et al.. (2017). Genome-Wide Convergence during Evolution of Mangroves from Woody Plants. Molecular Biology and Evolution. 34(4). msw277–msw277. 56 indexed citations
10.
Ren, Qian, Chunyang Wang, Min Jin, et al.. (2017). Co-option of bacteriophage lysozyme genes by bivalve genomes. Open Biology. 7(1). 160285–160285. 16 indexed citations
11.
Wyckoff, Gerald J., et al.. (2016). Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder. BioMed Research International. 2016. 1–11. 3 indexed citations
12.
13.
Barta, Michael L., Nicholas E. Dickenson, Andrew Keightley, et al.. (2012). The Structures of Coiled-Coil Domains from Type III Secretion System Translocators Reveal Homology to Pore-Forming Toxins. Journal of Molecular Biology. 417(5). 395–405. 59 indexed citations
14.
O’Bleness, Majesta, C. Michael Dickens, Laura Dumas, et al.. (2012). Evolutionary History and Genome Organization of DUF1220 Protein Domains. G3 Genes Genomes Genetics. 2(9). 977–986. 58 indexed citations
15.
Moldover, Brian, et al.. (2012). ChemVassa: A New Method for Identifying Small Molecule Hits in Drug Discovery. PubMed. 6(1). 29–34. 3 indexed citations
16.
Yang, Ming, et al.. (2010). Novel method for discerning the action of selection during evolution. Journal of Biomedical Science and Engineering. 3(2). 109–113. 1 indexed citations
17.
Nadeau, Owen W., David W. Anderson, Qing Yang, et al.. (2006). Evidence for the Location of the Allosteric Activation Switch in the Multisubunit Phosphorylase Kinase Complex from Mass Spectrometric Identification of Chemically Crosslinked Peptides. Journal of Molecular Biology. 365(5). 1429–1445. 22 indexed citations
18.
Dorus, Steve, Eric J. Vallender, Patrick Evans, et al.. (2004). Accelerated Evolution of Nervous System Genes in the Origin of Homo sapiens. Cell. 119(7). 1027–1040. 328 indexed citations
19.
Dorus, Steve, Patrick Evans, Gerald J. Wyckoff, Sun Shim Choi, & Bruce T. Lahn. (2004). Rate of molecular evolution of the seminal protein gene SEMG2 correlates with levels of female promiscuity. Nature Genetics. 36(12). 1326–1329. 189 indexed citations
20.
Fay, Justin C., Gerald J. Wyckoff, & Chung‐I Wu. (2002). Testing the neutral theory of molecular evolution with genomic data from Drosophila. Nature. 415(6875). 1024–1026. 261 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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