Melissa Basford

8.3k total citations · 1 hit paper
36 papers, 2.6k citations indexed

About

Melissa Basford is a scholar working on Genetics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Melissa Basford has authored 36 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Genetics, 8 papers in Molecular Biology and 7 papers in Artificial Intelligence. Recurrent topics in Melissa Basford's work include Genetic Associations and Epidemiology (11 papers), Genomics and Rare Diseases (7 papers) and Biomedical Text Mining and Ontologies (6 papers). Melissa Basford is often cited by papers focused on Genetic Associations and Epidemiology (11 papers), Genomics and Rare Diseases (7 papers) and Biomedical Text Mining and Ontologies (6 papers). Melissa Basford collaborates with scholars based in United States, Netherlands and Germany. Melissa Basford's co-authors include Joshua C. Denny, Dan M. Roden, Jill M. Pulley, Marylyn D. Ritchie, Dana C. Crawford, Kristin Brown‐Gentry, Dan Masys, Lisa Bastarache, Daniel R. Masys and Andrea H. Ramirez and has published in prestigious journals such as Circulation, Nature Communications and Bioinformatics.

In The Last Decade

Melissa Basford

36 papers receiving 2.6k citations

Hit Papers

align trajectories

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.

PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations

2010 703 citations Joshua C. Denny, Marylyn D. Ritchie et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Melissa Basford United States	19	942	803	537	373	296	36	2.6k
Daniel R. Masys United States	28	816 0.9×	714 0.9×	284 0.5×	348 0.9×	482 1.6×	75	3.0k
Peggy Peissig United States	28	972 1.0×	620 0.8×	844 1.6×	531 1.4×	302 1.0×	91	2.8k
Wei‐Qi Wei United States	25	1.1k 1.1×	1.0k 1.3×	680 1.3×	379 1.0×	288 1.0×	100	4.1k
Suzette J. Bielinski United States	33	844 0.9×	614 0.8×	413 0.8×	269 0.7×	353 1.2×	154	3.7k
Jill M. Pulley United States	27	893 0.9×	1.1k 1.3×	298 0.6×	201 0.5×	746 2.5×	81	3.7k
Luke V. Rasmussen United States	24	671 0.7×	384 0.5×	685 1.3×	497 1.3×	376 1.3×	83	2.1k
Omri Gottesman United States	19	519 0.6×	384 0.5×	302 0.6×	204 0.5×	148 0.5×	23	1.6k
Jennifer A. Pacheco United States	22	814 0.9×	417 0.5×	772 1.4×	521 1.4×	229 0.8×	63	2.2k
Lisa Bastarache United States	20	967 1.0×	895 1.1×	218 0.4×	97 0.3×	149 0.5×	60	3.0k
Katherine P. Liao United States	41	1.0k 1.1×	1.0k 1.3×	696 1.3×	328 0.9×	278 0.9×	164	5.2k

Countries citing papers authored by Melissa Basford

Since Specialization

Citations

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

Fields of papers citing papers by Melissa Basford

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melissa Basford

This figure shows the co-authorship network connecting the top 25 collaborators of Melissa Basford. A scholar is included among the top collaborators of Melissa Basford 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 Melissa Basford. Melissa Basford 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

Master, Hiral, et al.. (2025). Data from the All of Us research program reinforces existence of activity inequality. npj Digital Medicine. 8(1). 8–8. 2 indexed citations

Mapes, Brandy, Rachele Peterson, Karriem S. Watson, et al.. (2024). Informatics innovation to provide return of value to participant communities in the All of Us Research Program. Journal of the American Medical Informatics Association. 31(12). 3042–3046. 1 indexed citations

Master, Hiral, et al.. (2023). How Fitbit data are being made available to registered researchers in All of Us Research Program. 666–668. 2 indexed citations

Deflaux, Nicole, Margaret Sunitha Selvaraj, Kelsey Mayo, et al.. (2023). Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis. Nature Communications. 14(1). 5419–5419. 10 indexed citations

Ramadass, Karthik, Shunxing Bao, Melissa Basford, et al.. (2022). Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment. Journal of Digital Imaging. 35(4). 1023–1033. 7 indexed citations

Schildcrout, Jonathan S., Yaping Shi, Ioana Danciu, et al.. (2015). A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program. Journal of Clinical Epidemiology. 72. 107–115. 15 indexed citations

Danciu, Ioana, James D. Cowan, Melissa Basford, et al.. (2014). Secondary use of clinical data: The Vanderbilt approach. Journal of Biomedical Informatics. 52. 28–35. 201 indexed citations

Rosenbloom, S. Trent, Paul Harris, Jill M. Pulley, et al.. (2014). The Mid-South Clinical Data Research Network. Journal of the American Medical Informatics Association. 21(4). 627–632. 25 indexed citations

Newton, Katherine M., Peggy Peissig, Suzette J. Bielinski, et al.. (2013). Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association. 20(e1). e147–e154. 281 indexed citations

10.

Schildcrout, Jonathan S., Joshua C. Denny, Erica Bowton, et al.. (2012). Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping. Clinical Pharmacology & Therapeutics. 92(2). 235–242. 152 indexed citations

11.

Pathak, Jyotishman, Janey Wang, Melissa Basford, et al.. (2011). Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. Journal of the American Medical Informatics Association. 18(4). 376–386. 85 indexed citations

12.

Birdwell, Kelly A., Leena Choi, Aihua Bian, et al.. (2011). The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenetics and Genomics. 22(1). 32–42. 78 indexed citations

13.

Xu, Hua, Min Jiang, Erica Bowton, et al.. (2011). Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. Journal of the American Medical Informatics Association. 18(4). 387–391. 59 indexed citations

14.

Delaney, Jessica, Andrea H. Ramirez, Erica Bowton, et al.. (2011). Predicting Clopidogrel Response Using DNA Samples Linked to an Electronic Health Record. Clinical Pharmacology & Therapeutics. 91(2). 257–263. 76 indexed citations

15.

McGuire, Amy L., Melissa Basford, Lynn G. Dressler, et al.. (2011). Ethical and practical challenges of sharing data from genome-wide association studies: The eMERGE Consortium experience. Genome Research. 21(7). 1001–1007. 55 indexed citations

16.

Denny, Joshua C., Marylyn D. Ritchie, Melissa Basford, et al.. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics. 26(9). 1205–1210. 703 indexed citations breakdown →

17.

Dumitrescu, Logan, Marylyn D. Ritchie, Kristin Brown‐Gentry, et al.. (2010). Assessing the accuracy of observer-reported ancestry in a biorepository linked to electronic medical records. Genetics in Medicine. 12(10). 648–650. 76 indexed citations

18.

Ritchie, Marylyn D., Joshua C. Denny, Dana C. Crawford, et al.. (2010). Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record. The American Journal of Human Genetics. 87(2). 310–310. 3 indexed citations

19.

Ramirez, Andrea H., Jonathan S. Schildcrout, Dan Masys, et al.. (2010). Modulators of normal electrocardiographic intervals identified in a large electronic medical record. Heart Rhythm. 8(2). 271–277. 40 indexed citations

20.

Schildcrout, Jonathan S., Dan Masys, Jill M. Pulley, et al.. (2009). Abstract 2684: Modulators of Normal ECG Intervals Identified in a large Electronic Medical Record. Circulation. 120. 1 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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