Tim Putman
Impact in
- Microbiology top 5%
- Reproductive tract infections research
- Health Informatics top 10%
Papers in
-
- Reproductive tract infections research 6
-
- Biomedical Text Mining and Ontologies 14
- Genomics and Phylogenetic Studies 4
- Bioinformatics and Genomic Networks 2
- Co-authors
- Daniel D. Rockey (5 shared papers)Robert J. Suchland (4 shared papers)Andrew I. Su (7 shared papers)Chunlei Wu (4 shared papers)Gregory S. Stupp (3 shared papers)Cyrus Afrasiabi (2 shared papers)Nikhil Gopal (1 shared paper)Patricia L. Whetzel (1 shared paper)
- Journals
- Database (5 papers)Nucleic Acids Research (3 papers)Virus Research (1 paper)Genome biology (1 paper)The Journal of Infectious Diseases (1 paper)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Tim Putman
23 papers receiving 1k citations
Tim Putman's Hit Papers
Peers
Comparison fields: 5 of 110
- Microbiology 106
- Health Informatics 31
- Genetics 231
- Molecular Biology 460
- Artificial Intelligence 160
Countries citing papers authored by Tim Putman
This map shows the geographic impact of Tim Putman'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 Tim Putman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Putman more than expected).
Fields of papers citing papers by Tim Putman
This network shows the impact of papers produced by Tim Putman. 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 Tim Putman. The network helps show where Tim Putman may publish in the future.
Co-authors
The 25 scholars most cited alongside Tim Putman, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Human Phenotype Ontology in 2024: phenotypes around the world Hit paper breakdown → | 2023 | 204 |
| 2 | 2019 | 183 | |
| 3 | 2016 | 136 | |
| 4 | 2020 | 76 | |
| 5 | 2022 | 63 | |
| 6 | 2022 | 53 | |
| 7 | 2023 | 48 | |
| 8 | 2016 | 41 | |
| 9 | 2012 | 36 | |
| 10 | 2013 | 31 | |
| 11 | 2016 | 29 | |
| 12 | 2016 | 28 | |
| 13 | 2017 | 27 | |
| 14 | 2017 | 23 | |
| 15 | 2023 | 20 | |
| 16 | 2015 | 18 | |
| 17 | 2023 | 14 | |
| 18 | 2012 | 12 | |
| 19 | 2019 | 12 | |
| 20 | 2016 | 7 |
About Tim Putman
Tim Putman is a scholar working on Microbiology, Molecular Biology, Artificial Intelligence, Genetics and Health Information Management, having authored 23 papers that have together received 1.1k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (14 papers), Reproductive tract infections research (6 papers), Semantic Web and Ontologies (6 papers), Genomics and Phylogenetic Studies (4 papers), Bacterial Genetics and Biotechnology (3 papers), Bioinformatics and Genomic Networks (2 papers), Genomics and Rare Diseases (1 paper) and Health, Environment, Cognitive Aging (1 paper). The work is most often cited by research in Microbiology (106 citations), Health Informatics (31 citations), Genetics (231 citations), Molecular Biology (460 citations) and Artificial Intelligence (160 citations). Tim Putman has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Daniel D. Rockey, Robert J. Suchland, Andrew I. Su, Chunlei Wu, Gregory S. Stupp, Cyrus Afrasiabi, Nikhil Gopal, Patricia L. Whetzel, Andra Waagmeester and Ling Jin. Their work appears in journals such as Database, Nucleic Acids Research, Virus Research, Genome biology and The Journal of Infectious Diseases.
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.