Jason H. Moore
Impact in
- Genetics top 0.05%
- Genetic Associations and Epidemiology
- Genetic Mapping and Diversity in Plants and Animals
- Genetic and phenotypic traits in livestock
- Molecular Biology top 0.2%
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Epigenetics and DNA Methylation
Papers in
- Genetics 199
- Genetic Associations and Epidemiology 150
- Genetic Mapping and Diversity in Plants and Animals 48
- Co-authors
- Marylyn D. RitchieLance W. HahnScott M. WilliamsWilliam S. BushRyan J. UrbanowiczBill C. WhiteFolkert W. AsselbergsMargaret R. Karagas
- Journals
- BioData Mining (62 papers)Genetic Epidemiology (24 papers)Bioinformatics (19 papers)Human Genetics (16 papers)PLoS ONE (13 papers)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Jason H. Moore
600 papers receiving 26.3k citations
Hit Papers
Peers
Comparison fields: 5 of 221
- Genetics 8.5k
- Molecular Biology 12.6k
- Health Informatics 217
- Transplantation 387
- Cancer Research 2.1k
Countries citing papers authored by Jason H. Moore
This map shows the geographic impact of Jason H. Moore'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 Jason H. Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason H. Moore more than expected).
Fields of papers citing papers by Jason H. Moore
This network shows the impact of papers produced by Jason H. Moore. 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 Jason H. Moore. The network helps show where Jason H. Moore may publish in the future.
Co-authors
The 25 scholars most cited alongside Jason H. Moore, 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 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 6 | |
| 4 | 2023 | 4 | |
| 5 | 2023 | 4 | |
| 6 | 2023 | 5 | |
| 7 | 2023 | 2 | |
| 8 | 2021 | 29 | |
| 9 | 2020 | 1 | |
| 10 | 2020 | 14 | |
| 11 | 2020 | 43 | |
| 12 | Scaling tree-based automated machine learning to biomedical big data with a feature set selector Hit paper breakdown → | 2019 | 296 |
| 13 | 2019 | 2 | |
| 14 | 2019 | 4 | |
| 15 | 2018 | 16 | |
| 16 | 2018 | 2 | |
| 17 | 2017 | 3 | |
| 18 | 2017 | 6 | |
| 19 | 2016 | 0 | |
| 20 | Characterization of MicroRNA Expression Levels and Their Biological Correlates in Human Cancer Cell Lines Hit paper breakdown → | 2007 | 586 |
About Jason H. Moore
Jason H. Moore is a scholar working on Health Informatics, Genetics, Transplantation, Molecular Biology and Artificial Intelligence, having authored 635 papers that have together received 26.9k indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (150 papers), Bioinformatics and Genomic Networks (140 papers), Gene expression and cancer classification (102 papers), Evolutionary Algorithms and Applications (81 papers), Genetic Mapping and Diversity in Plants and Animals (48 papers), Metaheuristic Optimization Algorithms Research (40 papers), Gene Regulatory Network Analysis (31 papers) and Machine Learning in Healthcare (23 papers). The work is most often cited by research in Genetics (8.5k citations), Molecular Biology (12.6k citations), Health Informatics (217 citations), Transplantation (387 citations) and Cancer Research (2.1k citations). Jason H. Moore has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Marylyn D. Ritchie, Lance W. Hahn, Scott M. Williams, William S. Bush, Ryan J. Urbanowicz, Bill C. White, Folkert W. Asselbergs, Margaret R. Karagas, William D. Dupont and Nady Roodi. Their work appears in journals such as BioData Mining, Genetic Epidemiology, Bioinformatics, Human Genetics and PLoS ONE.
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.