Sayan Mukherjee
- Molecular Biology top 0.05%
- Cancer Research top 0.05%
- Oncology top 0.2%
- Immunology top 0.2%
- Pulmonary and Respiratory Medicine top 0.2%
- Co-authors
- Jill P. MesirovTodd R. GolubPablo TamayoEric S. LanderAravind SubramanianAmanda G. PaulovichMichael A. GilletteVamsi K. Mootha
- Topics
- Gene expression and cancer classification (26 papers)Bioinformatics and Genomic Networks (22 papers)Topological and Geometric Data Analysis (20 papers)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Sayan Mukherjee
134 papers receiving 42.6k citations
Hit Papers
Peers
Comparison fields: 5 of 218
- Molecular Biology 25.2k
- Cancer Research 8.3k
- Oncology 6.3k
- Immunology 6.0k
- Pulmonary and Respiratory Medicine 5.8k
Countries citing papers authored by Sayan Mukherjee
This map shows the geographic impact of Sayan Mukherjee'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 Sayan Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sayan Mukherjee more than expected).
Fields of papers citing papers by Sayan Mukherjee
This network shows the impact of papers produced by Sayan Mukherjee. 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 Sayan Mukherjee. The network helps show where Sayan Mukherjee may publish in the future.
Co-authorship network of co-authors of Sayan Mukherjee
This figure shows the co-authorship network connecting the top 25 collaborators of Sayan Mukherjee. A scholar is included among the top collaborators of Sayan Mukherjee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sayan Mukherjee. Sayan Mukherjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 12 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 8 | |
| 6 | 0 | |
| 7 | 23 | |
| 8 | 5 | |
| 9 | 9 | |
| 10 | 18 | |
| 11 | 61 | |
| 12 | Functional Data Analysis using a Topological Summary Statistic: the Smooth Euler Characteristic Transform | 6 |
| 13 | Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme | 4 |
| 14 | 26 | |
| 15 | Supervised Dimension Reduction Using Bayesian Mixture Modeling | 1 |
| 16 | 8 | |
| 17 | 28 | |
| 18 | Estimation of Gradients and Coordinate Covariation in Classification | 43 |
| 19 | Learning Coordinate Covariances via Gradients | 55 |
| 20 | 140 |
About Sayan Mukherjee
Sayan Mukherjee is a scholar working on Statistics and Probability, Computational Theory and Mathematics and Mathematical Physics, having authored 139 papers that have together received 43.3k indexed citations. Recurring topics across this work include Gene expression and cancer classification (26 papers), Bioinformatics and Genomic Networks (22 papers) and Topological and Geometric Data Analysis (20 papers). The work is most often cited by research in Cancer Research (8.3k citations), Molecular Biology (25.2k citations) and Immunology (6.0k citations). Sayan Mukherjee has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Jill P. Mesirov, Todd R. Golub, Pablo Tamayo, Eric S. Lander, Aravind Subramanian, Amanda G. Paulovich, Michael A. Gillette, Vamsi K. Mootha, Scott L. Pomeroy and Benjamin L. Ebert. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.
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.