Casey S. Greene
Impact in
- Health Informatics top 1%
- Molecular Biology top 2%
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Single-cell and spatial transcriptomics
- Biomedical Text Mining and Ontologies
- Machine Learning in Bioinformatics
Papers in
-
- Bioinformatics and Genomic Networks 45
- Gene expression and cancer classification 39
- Single-cell and spatial transcriptomics 14
- Biomedical Text Mining and Ontologies 13
- Molecular Biology Techniques and Applications 11
- Genomics and Phylogenetic Studies 10
- Co-authors
- Jason H. MooreBrett K. Beaulieu‐JonesGregory P. WayOlga G. TroyanskayaJie TanDaniel HimmelsteinNadia M. PenrodAaron K. Wong
- Journals
- BioData Mining (8 papers)PLoS Computational Biology (7 papers)GigaScience (5 papers)Nucleic Acids Research (4 papers)Genome biology (4 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Casey S. Greene
135 papers receiving 4.8k citations
Hit Papers
Peers
Comparison fields: 5 of 205
- Health Informatics 107
- Molecular Biology 2.8k
- Biophysics 178
- Cancer Research 457
- Genetics 787
Countries citing papers authored by Casey S. Greene
This map shows the geographic impact of Casey S. Greene'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 Casey S. Greene with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Casey S. Greene more than expected).
Fields of papers citing papers by Casey S. Greene
This network shows the impact of papers produced by Casey S. Greene. 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 Casey S. Greene. The network helps show where Casey S. Greene may publish in the future.
Co-authors
The 25 scholars most cited alongside Casey S. Greene, 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 | 2024 | 12 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 4 | |
| 5 | 2023 | 44 | |
| 6 | 2022 | 6 | |
| 7 | 2021 | 21 | |
| 8 | 2021 | 11 | |
| 9 | 2020 | 37 | |
| 10 | 2020 | 17 | |
| 11 | 2020 | 14 | |
| 12 | 2020 | 36 | |
| 13 | 2019 | 192 | |
| 14 | 2019 | 14 | |
| 15 | 2018 | 42 | |
| 16 | 2018 | 13 | |
| 17 | 2018 | 12 | |
| 18 | 2017 | 9 | |
| 19 | 2016 | 83 | |
| 20 | 2016 | 26 |
About Casey S. Greene
Casey S. Greene is a scholar working on Health Informatics, Molecular Biology, Cancer Research, Information Systems and Management and Discrete Mathematics and Combinatorics, having authored 144 papers that have together received 4.9k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (45 papers), Gene expression and cancer classification (39 papers), Single-cell and spatial transcriptomics (14 papers), Genetic Associations and Epidemiology (13 papers), Biomedical Text Mining and Ontologies (13 papers), Cancer-related molecular mechanisms research (12 papers), Molecular Biology Techniques and Applications (11 papers) and Genomics and Phylogenetic Studies (10 papers). The work is most often cited by research in Health Informatics (107 citations), Molecular Biology (2.8k citations), Biophysics (178 citations), Cancer Research (457 citations) and Genetics (787 citations). Casey S. Greene has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Jason H. Moore, Brett K. Beaulieu‐Jones, Gregory P. Way, Olga G. Troyanskaya, Jie Tan, Daniel Himmelstein, Nadia M. Penrod, Aaron K. Wong, David N. Nicholson and Jaclyn Taroni. Their work appears in journals such as BioData Mining, PLoS Computational Biology, GigaScience, Nucleic Acids Research and Genome biology.
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.