Jacob Frelinger
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
- Statistics and Probability top 10%
- Statistical Methods and Inference
Papers in
-
- Single-cell and spatial transcriptomics 8
- Gene expression and cancer classification 3
- Gene Regulatory Network Analysis 2
- Biomedical Text Mining and Ontologies 1
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- Bayesian Methods and Mixture Models 3
- Gaussian Processes and Bayesian Inference 1
- Co-authors
- Cliburn Chan (6 shared papers)Mike West (3 shared papers)Quanli Wang (1 shared paper)Marc A. Suchard (1 shared paper)Wenxin Jiang (2 shared papers)Greg Finak (2 shared papers)Raphaël Gottardo (2 shared papers)Stephen C. De Rosa (1 shared paper)
- Journals
- PLoS Computational Biology (2 papers)Cytometry Part A (2 papers)Cancer Immunology Immunotherapy (1 paper)Journal of Immunological Methods (1 paper)Journal of Computational and Graphical Statistics (1 paper)
- Partner nations
- United StatesGermanyNetherlands
In The Last Decade
Jacob Frelinger
9 papers receiving 355 citations
Peers
Comparison fields: 5 of 89
- Biophysics 75
- Statistics and Probability 47
- Computational Mathematics 2
- Immunology 68
- Molecular Biology 196
Countries citing papers authored by Jacob Frelinger
This map shows the geographic impact of Jacob Frelinger'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 Jacob Frelinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Frelinger more than expected).
Fields of papers citing papers by Jacob Frelinger
This network shows the impact of papers produced by Jacob Frelinger. 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 Jacob Frelinger. The network helps show where Jacob Frelinger may publish in the future.
Co-authors
The 25 scholars most cited alongside Jacob Frelinger, 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 | 2014 | 135 | |
| 2 | 2010 | 112 | |
| 3 | 2013 | 57 | |
| 4 | 2010 | 17 | |
| 5 | 2015 | 16 | |
| 6 | 2008 | 10 | |
| 7 | 2014 | 9 | |
| 8 | 2010 | 8 | |
| 9 | 2012 | 1 |
About Jacob Frelinger
Jacob Frelinger is a scholar working on Molecular Biology, Artificial Intelligence, Immunology, Biophysics and Infectious Diseases, having authored 9 papers that have together received 365 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (8 papers), Gene expression and cancer classification (3 papers), Bayesian Methods and Mixture Models (3 papers), T-cell and B-cell Immunology (2 papers), Gene Regulatory Network Analysis (2 papers), Cell Image Analysis Techniques (1 paper), Gaussian Processes and Bayesian Inference (1 paper) and Biomedical Text Mining and Ontologies (1 paper). The work is most often cited by research in Biophysics (75 citations), Statistics and Probability (47 citations), Computational Mathematics (2 citations), Immunology (68 citations) and Molecular Biology (196 citations). Jacob Frelinger has collaborated with scholars based in United States, Germany and Netherlands. Frequent co-authors include Cliburn Chan, Mike West, Quanli Wang, Marc A. Suchard, Wenxin Jiang, Greg Finak, Raphaël Gottardo, Stephen C. De Rosa, Evan W. Newell and Spyros A. Kalams. Their work appears in journals such as PLoS Computational Biology, Cytometry Part A, Cancer Immunology Immunotherapy, Journal of Immunological Methods and Journal of Computational and Graphical Statistics.
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.