Jay S. Stanley
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Single-cell and spatial transcriptomics
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Extracellular vesicles in disease
Papers in
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- Single-cell and spatial transcriptomics 4
- Gene expression and cancer classification 2
- Gene Regulatory Network Analysis 2
- RNA Research and Splicing 1
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- Cell Image Analysis Techniques 4
- Co-authors
- David van Dijk (3 shared papers)Smita Krishnaswamy (4 shared papers)Daniel B. Burkhardt (2 shared papers)Guy Wolf (4 shared papers)Kevin R. Moon (1 shared paper)Alexander Tong (2 shared papers)Kevan C. Herold (1 shared paper)Ana Luisa Perdigoto (1 shared paper)
- Journals
- Nature Biotechnology (2 papers)Current Opinion in Systems Biology (1 paper)Lecture notes in computer science (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Jay S. Stanley
6 papers receiving 175 citations
Peers
Comparison fields: 5 of 57
- Biophysics 44
- Molecular Biology 137
- Cancer Research 20
- Immunology 24
- Modeling and Simulation 4
Countries citing papers authored by Jay S. Stanley
This map shows the geographic impact of Jay S. Stanley'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 Jay S. Stanley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay S. Stanley more than expected).
Fields of papers citing papers by Jay S. Stanley
This network shows the impact of papers produced by Jay S. Stanley. 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 Jay S. Stanley. The network helps show where Jay S. Stanley may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay S. Stanley, 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 | 2021 | 84 | |
| 2 | 2017 | 73 | |
| 3 | 2024 | 12 | |
| 4 | 2020 | 3 | |
| 5 | Geometry Based Data Generation | 2018 | 2 |
| 6 | Manifold Alignment with Feature Correspondence. | 2018 | 1 |
About Jay S. Stanley
Jay S. Stanley is a scholar working on Molecular Biology, Biophysics, Control and Systems Engineering, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 6 papers that have together received 175 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (4 papers), Cell Image Analysis Techniques (4 papers), Gene expression and cancer classification (2 papers), Gene Regulatory Network Analysis (2 papers), RNA Research and Splicing (1 paper), Advanced Graph Neural Networks (1 paper), 3D Shape Modeling and Analysis (1 paper) and Human Motion and Animation (1 paper). The work is most often cited by research in Biophysics (44 citations), Molecular Biology (137 citations), Cancer Research (20 citations), Immunology (24 citations) and Modeling and Simulation (4 citations). Jay S. Stanley has collaborated with scholars based in United States and Canada. Frequent co-authors include David van Dijk, Smita Krishnaswamy, Daniel B. Burkhardt, Guy Wolf, Kevin R. Moon, Alexander Tong, Kevan C. Herold, Ana Luisa Perdigoto, Antonio J. Giráldez and Scott Gigante. Their work appears in journals such as Nature Biotechnology, Current Opinion in Systems Biology, Lecture notes in computer science and Neural Information Processing Systems.
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.