Robert Leaman
- Artificial Intelligence top 0.2%
- Topic Modeling 24
- Semantic Web and Ontologies 12
- Natural Language Processing Techniques 9
- Advanced Text Analysis Techniques 8
- Health Informatics top 1%
- Toxicology top 1%
- Molecular Biology top 2%
- Biomedical Text Mining and Ontologies 39
- Bioinformatics and Genomic Networks 5
- Machine Learning in Bioinformatics 3
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- Genomics and Rare Diseases 3
- Co-authors
- Zhiyong LuRezarta IslamajChih-Hsuan WeiGraciela Gonzalez‐HernandezRitu KhareAlexis AllotAllan Peter DavisThomas C. Wiegers
- Partner nations
- United StatesGermanyChina
In The Last Decade
Robert Leaman
45 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 142
- Artificial Intelligence 2.6k
- Health Informatics 90
- Toxicology 143
- Molecular Biology 2.8k
- Computational Theory and Mathematics 345
Countries citing papers authored by Robert Leaman
This map shows the geographic impact of Robert Leaman'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 Robert Leaman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Leaman more than expected).
Fields of papers citing papers by Robert Leaman
This network shows the impact of papers produced by Robert Leaman. 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 Robert Leaman. The network helps show where Robert Leaman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Robert Leaman, 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 | 2025 | 9 | |
| 2 | 2024 | 45 | |
| 3 | 2024 | 3 | |
| 4 | 2023 | 21 | |
| 5 | 2023 | 41 | |
| 6 | 2022 | 12 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 5 | |
| 9 | 2021 | 39 | |
| 10 | 2020 | 9 | |
| 11 | 2018 | 48 | |
| 12 | 2015 | 68 | |
| 13 | 2015 | 119 | |
| 14 | 2014 | 33 | |
| 15 | 2012 | 35 | |
| 16 | Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks | 2010 | 183 |
| 17 | 2010 | 30 | |
| 18 | 2008 | 241 | |
| 19 | Passage Relevancy Through Semantic Relatedness. | 2007 | 5 |
| 20 | ASU at TREC 2006 genomics track | 2006 | 2 |
About Robert Leaman
Robert Leaman is a scholar working on Health Informatics, Artificial Intelligence and Molecular Biology, having authored 46 papers that have together received 3.8k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (39 papers), Topic Modeling (24 papers), Semantic Web and Ontologies (12 papers), Natural Language Processing Techniques (9 papers), Advanced Text Analysis Techniques (8 papers), Bioinformatics and Genomic Networks (5 papers), Genomics and Rare Diseases (3 papers) and Machine Learning in Bioinformatics (3 papers). The work is most often cited by research in Artificial Intelligence (2.6k citations), Health Informatics (90 citations) and Toxicology (143 citations). Robert Leaman has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Zhiyong Lu, Rezarta Islamaj, Chih-Hsuan Wei, Graciela Gonzalez‐Hernandez, Ritu Khare, Alexis Allot, Allan Peter Davis, Thomas C. Wiegers, Jiao Li and Zhiyong Lu. Their work appears in journals such as Database, Bioinformatics, Journal of Biomedical Informatics, Nucleic Acids Research and Scientific Data.
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.