Sach Mukherjee
- Molecular Biology
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Statistics and Probability top 5%
- Computational Theory and Mathematics top 10%
- Co-authors
- Terence P. SpeedChris J. OatesWolfgang SchröderSteven M. HillJoe W. GrayRobert J. B. GoudieFrank DondelingerYiling Lu
- Topics
- Bioinformatics and Genomic Networks (17 papers)Gene Regulatory Network Analysis (13 papers)Gene expression and cancer classification (12 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaBioinformatics
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Sach Mukherjee
42 papers receiving 824 citations
Peers
Comparison fields: 5 of 137
- Molecular Biology 428
- Artificial Intelligence 181
- Computer Vision and Pattern Recognition 90
- Statistics and Probability 79
- Computational Theory and Mathematics 75
Countries citing papers authored by Sach Mukherjee
This map shows the geographic impact of Sach 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 Sach Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sach Mukherjee more than expected).
Fields of papers citing papers by Sach Mukherjee
This network shows the impact of papers produced by Sach 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 Sach Mukherjee. The network helps show where Sach Mukherjee may publish in the future.
Co-authorship network of co-authors of Sach Mukherjee
This figure shows the co-authorship network connecting the top 25 collaborators of Sach Mukherjee. A scholar is included among the top collaborators of Sach 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 Sach Mukherjee. Sach 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 | 2 | |
| 2 | 1 | |
| 3 | 22 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 80 | |
| 7 | 1 | |
| 8 | 60 | |
| 9 | 1 | |
| 10 | 23 | |
| 11 | 7 | |
| 12 | Causal Discovery as Semi-Supervised Learning | 1 |
| 13 | NETWORK INFERENCE AND BIOLOGICAL DYNAMICS1 | 28 |
| 14 | 25 | |
| 15 | 5 | |
| 16 | 20 | |
| 17 | 10 | |
| 18 | 3 | |
| 19 | 22 | |
| 20 | 21 |
About Sach Mukherjee
Sach Mukherjee is a scholar working on Statistics and Probability, Computational Theory and Mathematics and General Decision Sciences, having authored 43 papers that have together received 849 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (17 papers), Gene Regulatory Network Analysis (13 papers) and Gene expression and cancer classification (12 papers). The work is most often cited by research in Statistics and Probability (79 citations), Molecular Biology (428 citations) and Artificial Intelligence (181 citations). Sach Mukherjee has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Terence P. Speed, Chris J. Oates, Wolfgang Schröder, Steven M. Hill, Joe W. Gray, Robert J. B. Goudie, Frank Dondelinger, Yiling Lu, Gordon B. Mills and Paul T. Spellman. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Bioinformatics.
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.