John Mayer
Impact in
Papers in
-
- Epigenetics and DNA Methylation 3
- Biomedical Text Mining and Ontologies 3
- Genetics 9
- Genetic Associations and Epidemiology 7
- Genomics and Rare Diseases 4
- Co-authors
- Zhan Ye (9 shared papers)Scott J. Hebbring (11 shared papers)David Page (6 shared papers)Eric LaRose (3 shared papers)Peggy Peissig (3 shared papers)Murray H. Brilliant (4 shared papers)Majid Rastegar-Mojarad (1 shared paper)Simon Lin (1 shared paper)
- Journals
- Bioinformatics (3 papers)Science Translational Medicine (1 paper)Scientific Reports (1 paper)European Journal of Human Genetics (1 paper)Nature Communications (1 paper)
- Partner nations
- United StatesAustraliaSweden
In The Last Decade
John Mayer
16 papers receiving 285 citations
Peers
Comparison fields: 5 of 76
- Health Information Management 29
- Health Informatics 7
- Toxicology 16
- Genetics 99
- Periodontics 15
Countries citing papers authored by John Mayer
This map shows the geographic impact of John Mayer'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 John Mayer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Mayer more than expected).
Fields of papers citing papers by John Mayer
This network shows the impact of papers produced by John Mayer. 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 John Mayer. The network helps show where John Mayer may publish in the future.
Co-authors
The 25 scholars most cited alongside John Mayer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 74 | |
| 2 | 2014 | 35 | |
| 3 | 2015 | 33 | |
| 4 | 2017 | 32 | |
| 5 | 2019 | 30 | |
| 6 | 2016 | 27 | |
| 7 | 2017 | 13 | |
| 8 | 2014 | 9 | |
| 9 | 2016 | 8 | |
| 10 | 2017 | 6 | |
| 11 | 2023 | 6 | |
| 12 | 2022 | 5 | |
| 13 | 2023 | 4 | |
| 14 | 2021 | 2 | |
| 15 | 2019 | 1 | |
| 16 | 2021 | 1 | |
| 17 | 2024 | 0 |
About John Mayer
John Mayer is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Pathology and Forensic Medicine and Cardiology and Cardiovascular Medicine, having authored 17 papers that have together received 286 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (7 papers), Genomics and Rare Diseases (4 papers), Epigenetics and DNA Methylation (3 papers), Biomedical Text Mining and Ontologies (3 papers), Machine Learning in Healthcare (3 papers), Genetic factors in colorectal cancer (2 papers), Pharmacovigilance and Adverse Drug Reactions (1 paper) and MicroRNA in disease regulation (1 paper). The work is most often cited by research in Health Information Management (29 citations), Health Informatics (7 citations), Toxicology (16 citations), Genetics (99 citations) and Periodontics (15 citations). John Mayer has collaborated with scholars based in United States, Australia and Sweden. Frequent co-authors include Zhan Ye, Scott J. Hebbring, David Page, Eric LaRose, Peggy Peissig, Murray H. Brilliant, Majid Rastegar-Mojarad, Simon Lin, Elizabeth J. Phillips and Murray H. Brilliant. Their work appears in journals such as Bioinformatics, Science Translational Medicine, Scientific Reports, European Journal of Human Genetics and Nature Communications.
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.