Robert J. Steininger
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
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- Cancer Genomics and Diagnostics
Papers in
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- Cell Image Analysis Techniques 5
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- Single-cell and spatial transcriptomics 3
- Gene Regulatory Network Analysis 2
- Gene expression and cancer classification 1
- Co-authors
- Lani F. Wu (6 shared papers)Steven J. Altschuler (6 shared papers)Matthew Louis Hirsch (1 shared paper)Brian L. Ellis (1 shared paper)Jon Patrick Connelly (1 shared paper)Jenny C. Barker (1 shared paper)Matthew Hebden Porteus (1 shared paper)Satwik Rajaram (4 shared papers)
- Journals
- Journal of Visualized Experiments (2 papers)Molecular Systems Biology (1 paper)Nature Methods (1 paper)Nature Machine Intelligence (1 paper)The Breast Journal (1 paper)
- Partner nations
- United States
In The Last Decade
Robert J. Steininger
10 papers receiving 818 citations
Peers
Comparison fields: 5 of 92
- Biophysics 116
- Cancer Research 151
- Oncology 202
- Molecular Biology 540
- Genetics 182
Countries citing papers authored by Robert J. Steininger
This map shows the geographic impact of Robert J. Steininger'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 J. Steininger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert J. Steininger more than expected).
Fields of papers citing papers by Robert J. Steininger
This network shows the impact of papers produced by Robert J. Steininger. 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 J. Steininger. The network helps show where Robert J. Steininger may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert J. Steininger, 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 | 2016 | 438 | |
| 2 | 2013 | 202 | |
| 3 | 2010 | 100 | |
| 4 | 2009 | 61 | |
| 5 | 2024 | 15 | |
| 6 | 2014 | 10 | |
| 7 | 2019 | 5 | |
| 8 | 1979 | 3 | |
| 9 | 2014 | 2 | |
| 10 | 2014 | 1 |
About Robert J. Steininger
Robert J. Steininger is a scholar working on Biophysics, Molecular Biology, Computer Science Applications, Oncology and Dermatology, having authored 10 papers that have together received 837 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (5 papers), Single-cell and spatial transcriptomics (3 papers), Gene Regulatory Network Analysis (2 papers), Machine Learning and Data Classification (1 paper), Cancer Genomics and Diagnostics (1 paper), Cancer Cells and Metastasis (1 paper), Gene expression and cancer classification (1 paper) and Virus-based gene therapy research (1 paper). The work is most often cited by research in Biophysics (116 citations), Cancer Research (151 citations), Oncology (202 citations), Molecular Biology (540 citations) and Genetics (182 citations). Robert J. Steininger has collaborated with scholars based in United States. Frequent co-authors include Lani F. Wu, Steven J. Altschuler, Matthew Louis Hirsch, Brian L. Ellis, Jon Patrick Connelly, Jenny C. Barker, Matthew Hebden Porteus, Satwik Rajaram, Anwu Zhou and Maike A. Roth. Their work appears in journals such as Journal of Visualized Experiments, Molecular Systems Biology, Nature Methods, Nature Machine Intelligence and The Breast Journal.
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.