Mark Dane
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Cancer Cells and Metastasis
- HER2/EGFR in Cancer Research
Papers in
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- Single-cell and spatial transcriptomics 3
- Gene expression and cancer classification 2
- Advanced Biosensing Techniques and Applications 2
- Gene Regulatory Network Analysis 2
- FOXO transcription factor regulation 1
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- Cell Image Analysis Techniques 4
- Co-authors
- Laura M. Heiser (15 shared papers)Sean M. Gross (6 shared papers)Elmar Bucher (5 shared papers)Joe W. Gray (6 shared papers)Tiera Liby (3 shared papers)Gordon B. Mills (2 shared papers)James E. Korkola (8 shared papers)David Kilburn (4 shared papers)
- Journals
- Journal of Visualized Experiments (2 papers)Cell Systems (2 papers)Bioinformatics (1 paper)Human Molecular Genetics (1 paper)Cell Reports (1 paper)
- Partner nations
- United StatesNorway
In The Last Decade
Mark Dane
14 papers receiving 184 citations
Peers
Comparison fields: 5 of 67
- Biophysics 33
- Oncology 47
- Cancer Research 19
- Molecular Biology 91
- Modeling and Simulation 6
Countries citing papers authored by Mark Dane
This map shows the geographic impact of Mark Dane'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 Mark Dane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Dane more than expected).
Fields of papers citing papers by Mark Dane
This network shows the impact of papers produced by Mark Dane. 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 Mark Dane. The network helps show where Mark Dane may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Dane, 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 | 2018 | 57 | |
| 2 | 2022 | 30 | |
| 3 | 2023 | 28 | |
| 4 | 2019 | 23 | |
| 5 | 2022 | 11 | |
| 6 | 2019 | 10 | |
| 7 | 2020 | 8 | |
| 8 | 2017 | 8 | |
| 9 | 2020 | 8 | |
| 10 | 2022 | 2 | |
| 11 | 2025 | 1 | |
| 12 | 2019 | 1 | |
| 13 | 2019 | 1 | |
| 14 | 2005 | 1 | |
| 15 | 2025 | 0 | |
| 16 | 2023 | 0 |
About Mark Dane
Mark Dane is a scholar working on Molecular Biology, Biophysics, Oncology, Spectroscopy and Cellular and Molecular Neuroscience, having authored 16 papers that have together received 189 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (4 papers), Single-cell and spatial transcriptomics (3 papers), Advanced Proteomics Techniques and Applications (2 papers), Gene expression and cancer classification (2 papers), Advanced Biosensing Techniques and Applications (2 papers), Gene Regulatory Network Analysis (2 papers), Cancer Genomics and Diagnostics (1 paper) and FOXO transcription factor regulation (1 paper). The work is most often cited by research in Biophysics (33 citations), Oncology (47 citations), Cancer Research (19 citations), Molecular Biology (91 citations) and Modeling and Simulation (6 citations). Mark Dane has collaborated with scholars based in United States and Norway. Frequent co-authors include Laura M. Heiser, Sean M. Gross, Elmar Bucher, Joe W. Gray, Tiera Liby, Gordon B. Mills, James E. Korkola, David Kilburn, Rebecca Smith and Marilyne Labrie. Their work appears in journals such as Journal of Visualized Experiments, Cell Systems, Bioinformatics, Human Molecular Genetics and Cell Reports.
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.