Tina Pesaran

6.1k total citations · 2 hit papers
43 papers, 1.9k citations indexed

About

Tina Pesaran is a scholar working on Genetics, Molecular Biology and Cancer Research. According to data from OpenAlex, Tina Pesaran has authored 43 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Genetics, 19 papers in Molecular Biology and 17 papers in Cancer Research. Recurrent topics in Tina Pesaran's work include Genomics and Rare Diseases (20 papers), BRCA gene mutations in cancer (18 papers) and Cancer Genomics and Diagnostics (17 papers). Tina Pesaran is often cited by papers focused on Genomics and Rare Diseases (20 papers), BRCA gene mutations in cancer (18 papers) and Cancer Genomics and Diagnostics (17 papers). Tina Pesaran collaborates with scholars based in United States, Australia and United Kingdom. Tina Pesaran's co-authors include Jill S. Dolinsky, Heidi L. Rehm, Steven M. Harrison, Elizabeth Chao, Holly LaDuca, Leslie G. Biesecker, Ahmad Abou Tayoun, Andrea M. Oza, Marina T. DiStefano and Chunling Hu and has published in prestigious journals such as New England Journal of Medicine, Journal of Biological Chemistry and Journal of Clinical Oncology.

In The Last Decade

Tina Pesaran

41 papers receiving 1.8k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion

2018 433 citations Tina Pesaran, Heidi L. Rehm et al. Human Mutation profile →
Associations Between Cancer Predisposition Testing Panel Genes and Breast Cancer

2017 393 citations Fergus J. Couch, Hermela Shimelis et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tina Pesaran United States	16	1.2k	827	567	379	327	43	1.9k
Yuya Kobayashi United States	9	1.1k 0.9×	552 0.7×	428 0.8×	342 0.9×	208 0.6×	17	1.5k
Barbara Wappenschmidt Germany	27	732 0.6×	1.1k 1.4×	536 0.9×	233 0.6×	364 1.1×	62	1.7k
Bernard Peissel Italy	21	948 0.8×	942 1.1×	208 0.4×	265 0.7×	253 0.8×	63	1.7k
Sylvie Mazoyer France	25	1.5k 1.3×	1.6k 2.0×	615 1.1×	333 0.9×	274 0.8×	50	2.5k
Sunday M. Stray United States	11	1.3k 1.1×	921 1.1×	499 0.9×	428 1.1×	465 1.4×	14	2.2k
Jean McGowan‐Jordan Canada	15	675 0.6×	690 0.8×	208 0.4×	158 0.4×	108 0.3×	31	1.6k
Stewart J. Payne United Kingdom	19	369 0.3×	620 0.7×	523 0.9×	453 1.2×	424 1.3×	31	1.5k
Jan Osinga Netherlands	23	365 0.3×	754 0.9×	288 0.5×	225 0.6×	342 1.0×	34	1.5k
Sharon N. Teraoka United States	18	627 0.5×	830 1.0×	497 0.9×	167 0.4×	362 1.1×	27	1.3k
Åke Borg Sweden	18	594 0.5×	458 0.6×	270 0.5×	169 0.4×	379 1.2×	33	1.1k

Countries citing papers authored by Tina Pesaran

Since Specialization

Citations

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

Fields of papers citing papers by Tina Pesaran

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tina Pesaran. 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 Tina Pesaran. The network helps show where Tina Pesaran may publish in the future.

Co-authorship network of co-authors of Tina Pesaran

This figure shows the co-authorship network connecting the top 25 collaborators of Tina Pesaran. A scholar is included among the top collaborators of Tina Pesaran 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 Tina Pesaran. Tina Pesaran is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Fortuño, Cristina, Marcy E. Richardson, Tina Pesaran, et al.. (2025). Characteristics predicting reduced penetrance variants in the high-risk cancer predisposition gene TP53. Human Genetics and Genomics Advances. 6(4). 100484–100484.

Rañola, John Michael O., Carolyn Horton, Tina Pesaran, et al.. (2024). Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect. BMC Bioinformatics. 25(1). 295–295. 1 indexed citations

Biesecker, Leslie G., et al.. (2023). ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification. The American Journal of Human Genetics. 111(1). 24–38. 32 indexed citations

Iversen, Edwin S., Steven N. Hart, Kun Y. Lee, et al.. (2022). An integrative model for the comprehensive classification of BRCA1 and BRCA2 variants of uncertain clinical significance. npj Genomic Medicine. 7(1). 35–35. 4 indexed citations

Hernandez, Felicia, et al.. (2021). Classification of the canonical splice alteration MUTYH c.934-2A > G is likely benign based on RNA and clinical data. Molecular Case Studies. 8(1). a006152–a006152. 1 indexed citations

Fayer, Shawn, Carolyn Horton, Jennifer N. Dines, et al.. (2021). Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN. The American Journal of Human Genetics. 108(12). 2248–2258. 53 indexed citations

Richardson, Marcy E., Chunling Hu, Kun Y. Lee, et al.. (2021). Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance. The American Journal of Human Genetics. 108(3). 458–468. 29 indexed citations

Fortuño, Cristina, Jessica L. Mester, Tina Pesaran, et al.. (2020). Suggested application of HER2+ breast tumor phenotype for germline TP53 variant classification within ACMG/AMP guidelines. Human Mutation. 41(9). 1555–1562. 14 indexed citations

Hu, Chunling, Eric C. Polley, Siddhartha Yadav, et al.. (2020). The Contribution of Germline Predisposition Gene Mutations to Clinical Subtypes of Invasive Breast Cancer From a Clinical Genetic Testing Cohort. JNCI Journal of the National Cancer Institute. 112(12). 1231–1241. 62 indexed citations

10.

Fortuño, Cristina, Tina Pesaran, Jill S. Dolinsky, et al.. (2019). p53 major hotspot variants are associated with poorer prognostic features in hereditary cancer patients. Cancer Genetics. 235-236. 21–27. 11 indexed citations

11.

Tian, Yuan, Tina Pesaran, Adam Chamberlin, et al.. (2019). REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Scientific Reports. 9(1). 12752–12752. 58 indexed citations

12.

Qian, Dajun, Shuwei Li, Yuan Tian, et al.. (2018). A Bayesian framework for efficient and accurate variant prediction. PLoS ONE. 13(9). e0203553–e0203553. 10 indexed citations

13.

Mester, Jessica L., Rajarshi Ghosh, Tina Pesaran, et al.. (2018). Gene‐specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel. Human Mutation. 39(11). 1581–1592. 89 indexed citations

14.

Fortuño, Cristina, Paul A. James, Erin L. Young, et al.. (2018). Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants. Human Mutation. 39(8). 1061–1069. 27 indexed citations

15.

Richardson, Marcy E., Hansook Kim Chong, Wenbo Mu, et al.. (2018). DNA breakpoint assay reveals a majority of gross duplications occur in tandem reducing VUS classifications in breast cancer predisposition genes. Genetics in Medicine. 21(3). 683–693. 15 indexed citations

16.

Caleca, Laura, Irene Catucci, Gisella Figlioli, et al.. (2018). Two Missense Variants Detected in Breast Cancer Probands Preventing BRCA2-PALB2 Protein Interaction. Frontiers in Oncology. 8. 11 indexed citations

17.

Harrison, Steven M., Jill S. Dolinsky, Amy E. Knight Johnson, et al.. (2017). Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genetics in Medicine. 19(10). 1096–1104. 145 indexed citations

18.

Karam, Rachid, Tina Pesaran, & Elizabeth Chao. (2015). ClinGen and Genetic Testing.. New England Journal of Medicine. 373(14). 1376–1377. 1 indexed citations

19.

Millson, Alison, Tracey Lewis, Tina Pesaran, et al.. (2015). Processed Pseudogene Confounding Deletion/Duplication Assays for SMAD4. Journal of Molecular Diagnostics. 17(5). 576–582. 14 indexed citations

20.

LaDuca, Holly, Aaron J. Stuenkel, Jill S. Dolinsky, et al.. (2014). Utilization of multigene panels in hereditary cancer predisposition testing: analysis of more than 2,000 patients. Genetics in Medicine. 16(11). 830–837. 237 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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