Dan Masys

8 papers receiving 994 citations

Dan Masys's 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

Dan Masys
Comparison fields: 5 of 118
  • Genetics 396
  • Health Information Management 43
  • Molecular Biology 463
  • Cancer Research 80
  • Computational Mathematics 3
Replace Kristin Brown‐Gentry with:
Kristin Brown‐Gentry United States
Wendy A. Wolf United States
GR Bernard Gibraltar
Peter J. Castaldi United States
Lisa A. Bastarache United States
Jeffery L. Painter United States
Noura S. Abul‐Husn United States
DM Roden United States
R Thomas Lumbers United Kingdom
Jason H. Karnes United States
Dan Masys relative to Kristin Brown‐Gentry United States Kristin Brown‐Gentry's profile →
Citations per field
00.5×1.5×
Kristin Brown‐Gentry · 1×
Citations per year

Countries citing papers authored by Dan Masys

Since Specialization
Citations

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

Fields of papers citing papers by Dan Masys

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

8 of 8 papers shown
#Work
1
PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations
Hit paper breakdown →
2010703
2 2004147
3 200781
4 201040
5 201319
6 200713
7 201110
8
Abstract 2684: Modulators of Normal ECG Intervals Identified in a large Electronic Medical Record
20091

About Dan Masys

Dan Masys is a scholar working on Molecular Biology, General Health Professions, Public Health, Environmental and Occupational Health, Genetics and Infectious Diseases, having authored 8 papers that have together received 1.0k indexed citations. Recurring topics across this work include Ethics in Clinical Research (2 papers), Health Policy Implementation Science (1 paper), Cardiac electrophysiology and arrhythmias (1 paper), Genetic Associations and Epidemiology (1 paper), Health Sciences Research and Education (1 paper), HIV Research and Treatment (1 paper), Prostate Cancer Treatment and Research (1 paper) and HIV/AIDS Research and Interventions (1 paper). The work is most often cited by research in Genetics (396 citations), Health Information Management (43 citations), Molecular Biology (463 citations), Cancer Research (80 citations) and Computational Mathematics (3 citations). Dan Masys has collaborated with scholars based in United States, Switzerland and Australia. Frequent co-authors include Jill M. Pulley, Dan M. Roden, Joshua C. Denny, Melissa Basford, Marylyn D. Ritchie, Dana C. Crawford, Lisa Bastarache, Kristin Brown‐Gentry, Gordon R. Bernard and Steven Goodison. Their work appears in journals such as Pharmacogenomics, Circulation, HIV Clinical Trials, Proceedings of the National Academy of Sciences 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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