Melissa Basford

36 papers receiving 2.6k citations

Hit Papers

PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations 2010 · 703 citations
7030+5+10Years since publication200400600

Peers

Melissa Basford
Comparison fields: 5 of 141
  • Health Information Management 373
  • Pharmacology 285
  • Health Informatics 40
  • Computational Mathematics 17
  • Genetics 803
Replace Suzette J. Bielinski with:
Suzette J. Bielinski United States
Jill M. Pulley United States
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Citations per field
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Citations per year

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-authors

The 25 scholars most cited alongside Melissa Basford, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Melissa Basford Line = papers co-authored together Melissa Basford links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1
PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations
Hit paper breakdown →
2010703
2 2013281
3 2016238
4 2010237
5 2014201
6 2012152
7 201091
8 201185
9 201178
10 201076
11 201176
12 201270
13 201159
14 201155
15 201040
16 201425
17 201324
18 201020
19 201319
20 201515

About Melissa Basford

Melissa Basford is a scholar working on Genetics, Molecular Biology, Artificial Intelligence, Health Information Management and Pharmacology, having authored 36 papers that have together received 2.6k indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (11 papers), Genomics and Rare Diseases (7 papers), Biomedical Text Mining and Ontologies (6 papers), Pharmacogenetics and Drug Metabolism (5 papers), Electronic Health Records Systems (5 papers), Machine Learning in Healthcare (4 papers), Artificial Intelligence in Healthcare and Education (3 papers) and Scientific Computing and Data Management (3 papers). The work is most often cited by research in Health Information Management (373 citations), Pharmacology (285 citations), Health Informatics (40 citations), Computational Mathematics (17 citations) and Genetics (803 citations). Melissa Basford has collaborated with scholars based in United States, Netherlands and Germany. Frequent 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. Their work appears in journals such as Journal of the American Medical Informatics Association, Pharmacogenomics, Clinical Pharmacology & Therapeutics, Circulation and The American Journal of Human Genetics.

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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