Debarka Sengupta
Impact in
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- Biophysics top 2%
- Cell Image Analysis Techniques
Papers in
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- Single-cell and spatial transcriptomics 21
- Gene expression and cancer classification 10
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- Cancer Genomics and Diagnostics 9
- MicroRNA in disease regulation 9
- Co-authors
- Sanghamitra Bandyopadhyay (16 shared papers)Angshul Majumdar (7 shared papers)Aanchal Mongia (3 shared papers)Say Li Kong (2 shared papers)Huipeng Li (1 shared paper)Lawrence JK Wee (1 shared paper)Yuliana Tan (1 shared paper)Iain Beehuat Tan (1 shared paper)
In The Last Decade
Debarka Sengupta
53 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Cancer Research 537
- Biophysics 123
- Oncology 433
- Sensory Systems 75
- Molecular Biology 1.0k
Countries citing papers authored by Debarka Sengupta
This map shows the geographic impact of Debarka Sengupta'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 Debarka Sengupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debarka Sengupta more than expected).
Fields of papers citing papers by Debarka Sengupta
This network shows the impact of papers produced by Debarka Sengupta. 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 Debarka Sengupta. The network helps show where Debarka Sengupta may publish in the future.
Co-authors
The 25 scholars most cited alongside Debarka Sengupta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors Hit paper breakdown → | 2017 | 722 |
| 2 | 2018 | 118 | |
| 3 | 2018 | 83 | |
| 4 | 2018 | 78 | |
| 5 | 2022 | 70 | |
| 6 | 2019 | 55 | |
| 7 | 2020 | 43 | |
| 8 | 2020 | 42 | |
| 9 | 2018 | 38 | |
| 10 | 2018 | 29 | |
| 11 | 2020 | 28 | |
| 12 | 2015 | 26 | |
| 13 | 2013 | 25 | |
| 14 | 2021 | 24 | |
| 15 | 2021 | 20 | |
| 16 | 2020 | 19 | |
| 17 | 2019 | 19 | |
| 18 | 2011 | 18 | |
| 19 | 2019 | 17 | |
| 20 | 2020 | 15 |
About Debarka Sengupta
Debarka Sengupta is a scholar working on Molecular Biology, Cancer Research, Artificial Intelligence, Biomedical Engineering and Sensory Systems, having authored 57 papers that have together received 1.6k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (21 papers), Gene expression and cancer classification (10 papers), Cancer Genomics and Diagnostics (9 papers), MicroRNA in disease regulation (9 papers), Olfactory and Sensory Function Studies (6 papers), Cell Image Analysis Techniques (5 papers), Advanced Chemical Sensor Technologies (5 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Cancer Research (537 citations), Biophysics (123 citations), Oncology (433 citations), Sensory Systems (75 citations) and Molecular Biology (1.0k citations). Debarka Sengupta has collaborated with scholars based in India, Australia and Singapore. Frequent co-authors include Sanghamitra Bandyopadhyay, Angshul Majumdar, Aanchal Mongia, Say Li Kong, Huipeng Li, Lawrence JK Wee, Yuliana Tan, Iain Beehuat Tan, Clarinda Chua and Wah Siew Tan. Their work appears in journals such as Bioinformatics, Nucleic Acids Research, eLife, Genome Research and Nature Communications.
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.