Joshua C. Denny

51.3k total citations · 7 hit papers
340 papers, 17.2k citations indexed

About

Joshua C. Denny is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Joshua C. Denny has authored 340 papers receiving a total of 17.2k indexed citations (citations by other indexed papers that have themselves been cited), including 146 papers in Molecular Biology, 99 papers in Genetics and 79 papers in Artificial Intelligence. Recurrent topics in Joshua C. Denny's work include Biomedical Text Mining and Ontologies (104 papers), Genetic Associations and Epidemiology (64 papers) and Genomics and Rare Diseases (41 papers). Joshua C. Denny is often cited by papers focused on Biomedical Text Mining and Ontologies (104 papers), Genetic Associations and Epidemiology (64 papers) and Genomics and Rare Diseases (41 papers). Joshua C. Denny collaborates with scholars based in United States, China and United Kingdom. Joshua C. Denny's co-authors include Dan M. Roden, Robert J. Carroll, Wei‐Qi Wei, Lisa Bastarache, Hua Xu, Jill M. Pulley, Melissa Basford, Dana C. Crawford, Marylyn D. Ritchie and Anne E. Eyler and has published in prestigious journals such as New England Journal of Medicine, Cell and JAMA.

In The Last Decade

Joshua C. Denny

331 papers receiving 16.9k citations

Hit Papers

The “All of Us” Research ... 2010 2026 2015 2020 2019 2015 2010 2018 2019 250 500 750 1000
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.

  1. The “All of Us” Research Program
    2019 1.1k citations Joshua C. Denny profile →
  2. A gene-based association method for mapping traits using reference transcriptome data
    2015 975 citations Robert J. Carroll, Joshua C. Denny et al. profile →
  3. 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 →
  4. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies
    2018 596 citations Lars G. Fritsche, Lisa A. Bastarache et al. profile →
  5. Artificial intelligence, bias and clinical safety
    2019 542 citations Joshua C. Denny et al. profile →
  6. Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
    2019 255 citations Robert J. Carroll, Lisa Bastarache et al. profile →
  7. Precision medicine in 2030—seven ways to transform healthcare
    2021 187 citations Joshua C. Denny et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joshua C. Denny United States 67 5.9k 4.3k 3.9k 2.0k 1.7k 340 17.2k
Isaac S. Kohane United States 87 12.6k 2.1× 4.6k 1.1× 5.0k 1.3× 2.9k 1.5× 3.1k 1.8× 470 34.8k
Nigam H. Shah United States 62 5.5k 0.9× 815 0.2× 5.2k 1.3× 1.6k 0.8× 1.2k 0.7× 287 15.5k
Christopher G. Chute United States 59 5.5k 0.9× 1.1k 0.2× 4.8k 1.2× 2.0k 1.0× 1.4k 0.8× 351 14.9k
Atul J. Butte United States 76 15.9k 2.7× 2.8k 0.7× 1.3k 0.3× 434 0.2× 1.2k 0.7× 338 29.2k
George Hripcsak United States 63 5.2k 0.9× 600 0.1× 5.3k 1.3× 3.9k 1.9× 1.7k 1.0× 371 17.0k
Marylyn D. Ritchie United States 60 6.9k 1.2× 6.2k 1.4× 852 0.2× 252 0.1× 583 0.3× 329 15.5k
Li Li China 53 4.9k 0.8× 946 0.2× 1.1k 0.3× 577 0.3× 1.1k 0.6× 699 14.9k
Sebastian Schneeweiß United States 98 2.1k 0.4× 995 0.2× 687 0.2× 635 0.3× 2.2k 1.3× 564 35.1k
Kenneth D. Mandl United States 62 1.2k 0.2× 561 0.1× 1.5k 0.4× 1.9k 0.9× 2.5k 1.4× 299 12.9k
Shawn N. Murphy United States 51 2.6k 0.4× 1.2k 0.3× 1.8k 0.5× 1.4k 0.7× 992 0.6× 214 8.8k

Countries citing papers authored by Joshua C. Denny

Since Specialization
Citations

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

Fields of papers citing papers by Joshua C. Denny

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joshua C. Denny. 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 Joshua C. Denny. The network helps show where Joshua C. Denny may publish in the future.

Co-authorship network of co-authors of Joshua C. Denny

This figure shows the co-authorship network connecting the top 25 collaborators of Joshua C. Denny. A scholar is included among the top collaborators of Joshua C. Denny 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 Joshua C. Denny. Joshua C. Denny 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.
Schlueter, David J., et al.. (2024). PheWAS analysis on large-scale biobank data with PheTK. Bioinformatics. 41(1). 3 indexed citations
2.
Goleva, Slavina B., et al.. (2024). Racial and Ethnic Disparities in Antihypertensive Medication Prescribing Patterns and Effectiveness. Clinical Pharmacology & Therapeutics. 116(6). 1544–1553. 3 indexed citations
3.
Barnado, April, et al.. (2023). Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record. Arthritis & Rheumatology. 75(9). 1532–1541. 2 indexed citations
4.
Feng, Yen‐Chen Anne, Ian B. Stanaway, John J. Connolly, et al.. (2022). Psychiatric manifestations of rare variation in medically actionable genes: a PheWAS approach. BMC Genomics. 23(1). 385–385. 4 indexed citations
5.
Nadkarni, Girish N., Kezhen Fei, Geneviève Galarneau, et al.. (2021). APOL1 renal risk variants are associated with obesity and body composition in African ancestry adults: An observational genotype-phenotype association study.. PubMed. 100(45). e27785–e27785. 3 indexed citations
6.
Goldstein, Jeffrey A., Joshua S. Weinstock, Lisa A. Bastarache, et al.. (2020). LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks. PLoS Genetics. 16(11). e1009077–e1009077. 13 indexed citations
7.
McNeer, Elizabeth, et al.. (2019). medExtractR: A targeted, customizable approach to medication extraction from electronic health records. Journal of the American Medical Informatics Association. 27(3). 407–418. 19 indexed citations
8.
Gill, Dipender, Marios K. Georgakis, Fotios Koskeridis, et al.. (2019). Use of Genetic Variants Related to Antihypertensive Drugs to Inform on Efficacy and Side Effects. Circulation. 140(4). 270–279. 89 indexed citations
9.
Breen, Nancy, David Berrigan, James S. Jackson, et al.. (2019). Translational Health Disparities Research in a Data-Rich World. Health Equity. 3(1). 588–600. 25 indexed citations
10.
11.
Karnes, Jason H., Lisa Bastarache, Christian M. Shaffer, et al.. (2017). Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Science Translational Medicine. 9(389). 74 indexed citations
12.
Pulley, Jill M., Jana K. Shirey-Rice, Robert R. Lavieri, et al.. (2017). Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics. Assay and Drug Development Technologies. 15(3). 113–119. 30 indexed citations
13.
Jerome, Rebecca N, Jill M. Pulley, Dan M. Roden, et al.. (2017). Using Human ‘Experiments of Nature’ to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors. Drug Safety. 41(3). 303–311. 17 indexed citations
14.
Denny, Joshua C., Lisa Bastarache, & Dan M. Roden. (2016). Phenome-Wide Association Studies as a Tool to Advance Precision Medicine. Annual Review of Genomics and Human Genetics. 17(1). 353–373. 149 indexed citations
15.
Rasmussen, Luke V., Jie Xu, Jennifer A. Pacheco, et al.. (2014). Evaluation of Existing Phenotype Authoring Tools for Clinical Research.. AMIA. 1 indexed citations
16.
Zhang, Yaoyun, et al.. (2014). A Preliminary Study of Coupling Transfer Learning with Active Learning for Clinical Named Entity Recognition between Two Institutions.. AMIA. 1 indexed citations
17.
Tang, Buzhou, Yonghui Wu, Min Jiang, Joshua C. Denny, & Hua Xu. (2013). Recognizing and Encoding Disorder Concepts in Clinical Text using Machine Learning and Vector Space Model. CLEF (Working Notes). 1179. 26 indexed citations
18.
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 →
19.
Spickard, Anderson, et al.. (2008). Automatic Capture of Student Notes to Augment Mentor Feedback and Student Performance on Patient Write-Ups. Journal of General Internal Medicine. 23(7). 979–984. 27 indexed citations
20.
Denny, Joshua C., et al.. (2002). A New Tool to Identify Key Biomedical Concepts in Text Documents, with Special Application to Curriculum Content. PubMed Central. 1007–1007. 11 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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