Fred A. Wright

42.1k total citations
195 papers, 8.2k citations indexed

About

Fred A. Wright is a scholar working on Molecular Biology, Genetics and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Fred A. Wright has authored 195 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Molecular Biology, 66 papers in Genetics and 27 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Fred A. Wright's work include Gene expression and cancer classification (33 papers), Genetic Associations and Epidemiology (30 papers) and Genetic Mapping and Diversity in Plants and Animals (27 papers). Fred A. Wright is often cited by papers focused on Gene expression and cancer classification (33 papers), Genetic Associations and Epidemiology (30 papers) and Genetic Mapping and Diversity in Plants and Animals (27 papers). Fred A. Wright collaborates with scholars based in United States, Canada and United Kingdom. Fred A. Wright's co-authors include Ivan Rusyn, Albert de la Chapelle, Fei Zou, William T. Barry, Andrew B. Nobel, Weihsueh A. Chiu, Yi‐Hui Zhou, William J. Lemon, Ralf Krahe and Michael R. Knowles and has published in prestigious journals such as Nature, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

In The Last Decade

Fred A. Wright

183 papers receiving 8.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fred A. Wright United States 50 3.7k 1.8k 954 836 767 195 8.2k
David V. Conti United States 42 2.2k 0.6× 1.8k 1.0× 546 0.6× 784 0.9× 578 0.8× 228 6.5k
Michael C. Wu United States 42 3.8k 1.0× 2.5k 1.4× 522 0.5× 462 0.6× 825 1.1× 154 7.9k
Jack A. Taylor United States 59 5.7k 1.6× 2.2k 1.3× 1.7k 1.8× 872 1.0× 1.4k 1.8× 220 11.0k
Yun Liu China 45 4.5k 1.2× 1.8k 1.0× 967 1.0× 764 0.9× 600 0.8× 429 9.8k
Chu Chen United States 55 3.6k 1.0× 2.1k 1.2× 1.5k 1.6× 1.0k 1.2× 1.9k 2.5× 294 10.4k
Celia M.T. Greenwood Canada 45 2.8k 0.8× 2.0k 1.1× 749 0.8× 898 1.1× 692 0.9× 217 8.2k
Marina Sirota United States 37 4.2k 1.1× 1.3k 0.7× 1.1k 1.2× 693 0.8× 787 1.0× 141 7.9k
John S. Witte United States 53 2.8k 0.8× 2.6k 1.5× 1.1k 1.2× 1.8k 2.1× 1.3k 1.7× 220 8.5k
Liming Liang United States 47 3.5k 1.0× 2.0k 1.1× 574 0.6× 626 0.7× 345 0.4× 269 8.2k
Jia Chen United States 45 2.2k 0.6× 759 0.4× 790 0.8× 438 0.5× 1.1k 1.4× 304 6.8k

Countries citing papers authored by Fred A. Wright

Since Specialization
Citations

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

Fields of papers citing papers by Fred A. Wright

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fred A. Wright

This figure shows the co-authorship network connecting the top 25 collaborators of Fred A. Wright. A scholar is included among the top collaborators of Fred A. Wright 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 Fred A. Wright. Fred A. Wright 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.
Perrin, Hannah J., Swarooparani Vadlamudi, Amy S. Etheridge, et al.. (2025). Genetic effects on chromatin accessibility uncover mechanisms of liver gene regulation and quantitative traits. Genome Research. 35(7). 1485–1502.
2.
3.
Zhou, Yi‐Hui, Paul J. Gallins, Ivan Rusyn, & Fred A. Wright. (2025). An approach to uncover significant direct and mediated relationships in multi-dimensional new approach methods (NAMs) data: A case study of hazard evaluation of petroleum UVCBs. The Science of The Total Environment. 985. 179724–179724.
4.
Broadaway, K. Alaine, Sarah M. Brotman, Jonathan D. Rosen, et al.. (2024). Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits. The American Journal of Human Genetics. 111(9). 1899–1913. 5 indexed citations
5.
Ball, Nicholas, et al.. (2024). Systematic analysis of read-across adaptations in testing proposal evaluations by the European Chemicals Agency. ALTEX. 42(1). 22–38. 5 indexed citations
6.
7.
Zhou, Yi‐Hui, Fred A. Wright, Alexander Sedykh, et al.. (2024). Hazard and risk characterization of 56 structurally diverse PFAS using a targeted battery of broad coverage assays using six human cell types. Toxicology. 503. 153763–153763. 8 indexed citations
8.
Stonebraker, Jaclyn R., Rhonda G. Pace, Paul J. Gallins, et al.. (2024). Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis. Hepatology. 80(5). 1012–1025. 2 indexed citations
9.
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11.
Jima, Dereje D., David Skaar, Antonio Planchart, et al.. (2022). Genomic map of candidate human imprint control regions: the imprintome. Epigenetics. 17(13). 1920–1943. 34 indexed citations
12.
Mukherjee, Rajib, Burcu Beykal, Adam T. Szafran, et al.. (2020). Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms. PLoS Computational Biology. 16(9). e1008191–e1008191. 12 indexed citations
13.
Onel, Melis, Burcu Beykal, Kyle Ferguson, et al.. (2019). Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization. PLoS ONE. 14(10). e0223517–e0223517. 22 indexed citations
14.
Li, Gen, Dereje D. Jima, Fred A. Wright, & Andrew B. Nobel. (2017). HT-eQTL: Integrative eQTL Analysis in a Large Number of Human Tissues. arXiv (Cornell University). 1 indexed citations
15.
Polineni, Deepika, Hong Dang, Paul J. Gallins, et al.. (2017). Airway Mucosal Host Defense Is Key to Genomic Regulation of Cystic Fibrosis Lung Disease Severity. American Journal of Respiratory and Critical Care Medicine. 197(1). 79–93. 35 indexed citations
16.
Abdo, Nour, Menghang Xia, Chad Brown, et al.. (2015). Population-Based in Vitro Hazard and Concentration–Response Assessment of Chemicals: The 1000 Genomes High-Throughput Screening Study. Environmental Health Perspectives. 123(5). 458–466. 74 indexed citations
17.
Byrnes, Andrea, Karin Dahlman‐Wright, Birgitta Evengård, et al.. (2009). Gene Expression in Peripheral Blood Leukocytes in Monozygotic Twins Discordant for Chronic Fatigue: No Evidence of a Biomarker. PLoS ONE. 4(6). e5805–e5805. 18 indexed citations
18.
Virtaneva, Kimmo, Fred A. Wright, Stephan M. Tanner, et al.. (2001). Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics. Proceedings of the National Academy of Sciences. 98(3). 1124–1129. 226 indexed citations
19.
Zhuo, Degen, Wei Zhao, Fred A. Wright, et al.. (2001). Assembly, Annotation, and Integration of UNIGENE Clusters into the Human Genome Draft. Genome Research. 11(5). 904–918. 13 indexed citations
20.
Newman, Vicky A., Cheryl L. Rock, Susan Faerber, et al.. (1998). Dietary Supplement Use by Women at Risk for Breast Cancer Recurrence. Journal of the American Dietetic Association. 98(3). 285–292. 94 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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