Saumyadipta Pyne
- Statistics and Probability top 1%
- Statistical Methods and Inference 4
- Biophysics top 2%
- Artificial Intelligence top 5%
- Bayesian Methods and Mixture Models 7
- Molecular Biology top 10%
- Gene expression and cancer classification 20
- Single-cell and spatial transcriptomics 14
- Bioinformatics and Genomic Networks 7
- Gene Regulatory Network Analysis 5
- Fungal and yeast genetics research 4
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- Genetic Mapping and Diversity in Plants and Animals 4
- Co-authors
- Sylvia Frühwirth‐SchnatterTsung‐I LinGeoffrey J. McLachlanJill P. MesirovDavid A. HaflerElizabeth J. RossinPablo TamayoPhilip L. De Jager
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Saumyadipta Pyne
53 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 134
- Statistics and Probability 279
- Biophysics 131
- Artificial Intelligence 363
- Molecular Biology 775
- Cancer Research 124
Countries citing papers authored by Saumyadipta Pyne
This map shows the geographic impact of Saumyadipta Pyne'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 Saumyadipta Pyne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saumyadipta Pyne more than expected).
Fields of papers citing papers by Saumyadipta Pyne
This network shows the impact of papers produced by Saumyadipta Pyne. 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 Saumyadipta Pyne. The network helps show where Saumyadipta Pyne may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Saumyadipta Pyne, 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 | 2025 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 1 | |
| 5 | 2022 | 1 | |
| 6 | 2020 | 2 | |
| 7 | 2019 | 1 | |
| 8 | 2018 | 5 | |
| 9 | 2018 | 2 | |
| 10 | 2017 | 11 | |
| 11 | 2016 | 7 | |
| 12 | 2016 | 4 | |
| 13 | COMPARATIVE BIOMETRICS AND PERFORMANCES OF THREE COLOUR VARIETIES OF BENGAL GOATS IN THEIR HOME TRACT | 2015 | 5 |
| 14 | Nature and man: the goal of bio-security in the course of rapid and inevitable human development | 2015 | 3 |
| 15 | 2014 | 112 | |
| 16 | 2014 | 9 | |
| 17 | 2013 | 9 | |
| 18 | 2012 | 9 | |
| 19 | 2010 | 38 | |
| 20 | 2005 | 156 |
About Saumyadipta Pyne
Saumyadipta Pyne is a scholar working on Modeling and Simulation, Statistics and Probability and Molecular Biology, having authored 57 papers that have together received 1.4k indexed citations. Recurring topics across this work include Gene expression and cancer classification (20 papers), Single-cell and spatial transcriptomics (14 papers), Bayesian Methods and Mixture Models (7 papers), Bioinformatics and Genomic Networks (7 papers), Gene Regulatory Network Analysis (5 papers), Genetic Mapping and Diversity in Plants and Animals (4 papers), Statistical Methods and Inference (4 papers) and Fungal and yeast genetics research (4 papers). The work is most often cited by research in Statistics and Probability (279 citations), Biophysics (131 citations) and Artificial Intelligence (363 citations). Saumyadipta Pyne has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Sylvia Frühwirth‐Schnatter, Tsung‐I Lin, Geoffrey J. McLachlan, Jill P. Mesirov, David A. Hafler, Elizabeth J. Rossin, Pablo Tamayo, Philip L. De Jager, Xinli Hu and Lisa M. Maier. Their work appears in journals such as PLoS ONE, BMC Bioinformatics, Bioinformatics, Computers in Biology and Medicine and Computational Statistics & Data Analysis.
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.