Bethany Percha

1.7k citations
25 papers · 942 indexed · h-index 16

Impact in

Papers in

    • Biomedical Text Mining and Ontologies 11
    • Topic Modeling 4
    • Natural Language Processing Techniques 3
    • Advanced Text Analysis Techniques 3
    • Machine Learning in Healthcare 2

Bethany Percha

25 papers receiving 925 citations

Peers

Bethany Percha
Comparison fields: 5 of 127
  • Health Informatics 27
  • Toxicology 51
  • Computational Theory and Mathematics 230
  • Pharmacology 98
  • Health Information Management 42
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Citations per field
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Citations per year

Countries citing papers authored by Bethany Percha

Since Specialization
Citations

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

Fields of papers citing papers by Bethany Percha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Bethany Percha, 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 Bethany Percha Line = papers co-authored together Bethany Percha links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013157
2 2005119
3 201197
4 201882
5 200664
6 202060
7 201246
8 201342
9 201440
10 201432
11 201530
12 202129
13 201128
14 201922
15 201719
16 201917
17 202114
18 202011
19 20217
20 20207

About Bethany Percha

Bethany Percha is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Public Health, Environmental and Occupational Health and Infectious Diseases, having authored 25 papers that have together received 942 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (11 papers), Computational Drug Discovery Methods (4 papers), Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Machine Learning in Healthcare (2 papers) and Antibiotic Use and Resistance (2 papers). The work is most often cited by research in Health Informatics (27 citations), Toxicology (51 citations), Computational Theory and Mathematics (230 citations), Pharmacology (98 citations) and Health Information Management (42 citations). Bethany Percha has collaborated with scholars based in United States, Türkiye and Canada. Frequent co-authors include Russ B. Altman, Yael Garten, Rhonda Dzakpasu, Michał Żochowski, Jack M. Parent, Betsy Foxman, M. E. J. Newman, Sevgi O. Aral, King K. Holmes and Nigam H. Shah. Their work appears in journals such as Journal of the American Medical Informatics Association, Infection Genetics and Evolution, Sexually Transmitted Diseases, Blood and Bioinformatics.

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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