David Hess
Impact in
- Aging top 5%
- Clinical Biochemistry top 5%
- Metabolism and Genetic Disorders
Papers in
-
- Fungal and yeast genetics research 8
- Bioinformatics and Genomic Networks 7
- Microbial Metabolic Engineering and Bioproduction 4
- Machine Learning in Bioinformatics 2
-
- Antifungal resistance and susceptibility 4
- Co-authors
- Olga G. Troyanskaya (8 shared papers)Chad L. Myers (7 shared papers)Matthew Hibbs (5 shared papers)Amy A. Caudy (6 shared papers)David Botstein (3 shared papers)Curtis Huttenhower (5 shared papers)Kai Li (2 shared papers)Wenyun Lu (1 shared paper)
- Journals
- Bioinformatics (2 papers)Antimicrobial Agents and Chemotherapy (2 papers)PLoS Computational Biology (2 papers)Genome biology (2 papers)Frontiers in Public Health (2 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
David Hess
27 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 108
- Aging 44
- Clinical Biochemistry 145
- Molecular Biology 1.0k
- Microbiology 45
- Molecular Medicine 32
Countries citing papers authored by David Hess
This map shows the geographic impact of David Hess'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 David Hess with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Hess more than expected).
Fields of papers citing papers by David Hess
This network shows the impact of papers produced by David Hess. 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 David Hess. The network helps show where David Hess may publish in the future.
Co-authors
The 25 scholars most cited alongside David Hess, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 255 | |
| 2 | 2007 | 185 | |
| 3 | 1990 | 140 | |
| 4 | 2006 | 130 | |
| 5 | 2009 | 118 | |
| 6 | 2008 | 107 | |
| 7 | 2013 | 88 | |
| 8 | 2014 | 52 | |
| 9 | 2020 | 40 | |
| 10 | 2003 | 31 | |
| 11 | 2009 | 29 | |
| 12 | 2009 | 27 | |
| 13 | 2012 | 25 | |
| 14 | 2005 | 20 | |
| 15 | 2012 | 15 | |
| 16 | 2010 | 15 | |
| 17 | 2013 | 15 | |
| 18 | 2023 | 13 | |
| 19 | 2015 | 12 | |
| 20 | 2023 | 9 |
About David Hess
David Hess is a scholar working on Molecular Biology, Infectious Diseases, Microbiology, Physiology and Plant Science, having authored 30 papers that have together received 1.3k indexed citations. Recurring topics across this work include Fungal and yeast genetics research (8 papers), Bioinformatics and Genomic Networks (7 papers), Reproductive tract infections research (5 papers), Bacterial Infections and Vaccines (5 papers), Microbial Metabolic Engineering and Bioproduction (4 papers), Antifungal resistance and susceptibility (4 papers), Syphilis Diagnosis and Treatment (4 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Aging (44 citations), Clinical Biochemistry (145 citations), Molecular Biology (1.0k citations), Microbiology (45 citations) and Molecular Medicine (32 citations). David Hess has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Olga G. Troyanskaya, Chad L. Myers, Matthew Hibbs, Amy A. Caudy, David Botstein, Curtis Huttenhower, Kai Li, Wenyun Lu, Joshua D. Rabinowitz and Pamela Arn. Their work appears in journals such as Bioinformatics, Antimicrobial Agents and Chemotherapy, PLoS Computational Biology, Genome biology and Frontiers in Public Health.
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.