Pradeep Kumar Das
- Aging top 1%
- Plant Science top 0.5%
- Plant Molecular Biology Research 18
- Molecular Biology top 2%
- Plant Reproductive Biology 17
- TGF-β signaling in diseases 6
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- Digital Imaging for Blood Diseases 16
- Biophysics top 2%
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- COVID-19 diagnosis using AI 14
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- AI in cancer detection 13
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- Brain Tumor Detection and Classification 5
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- Metal complexes synthesis and properties 5
- Co-authors
- Sukadev MeherElliot M. MeyerowitzMarcus G. HeislerG. Venugopala ReddyCarolyn OhnoRichard W. PadgettPatrick SieberJeff A. Long
- Cited by
- AgingPlant ScienceMolecular Biology
- Journals
- Development (4 papers)Proceedings of the National Academy of Sciences (3 papers)Engineering Applications of Artificial Intelligence (3 papers)
- Partner nations
- IndiaUnited StatesFrance
In The Last Decade
Pradeep Kumar Das
66 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Aging 172
- Plant Science 2.4k
- Molecular Biology 2.9k
- Computer Vision and Pattern Recognition 541
- Biophysics 148
Countries citing papers authored by Pradeep Kumar Das
This map shows the geographic impact of Pradeep Kumar Das'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 Pradeep Kumar Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pradeep Kumar Das more than expected).
Fields of papers citing papers by Pradeep Kumar Das
This network shows the impact of papers produced by Pradeep Kumar Das. 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 Pradeep Kumar Das. The network helps show where Pradeep Kumar Das may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pradeep Kumar Das, 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 | 2 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 16 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 4 | |
| 8 | 2023 | 16 | |
| 9 | An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast imagesbreakdown → | 2023 | 93 |
| 10 | 2022 | 90 | |
| 11 | 2022 | 58 | |
| 12 | 2021 | 84 | |
| 13 | 2020 | 14 | |
| 14 | 2020 | 16 | |
| 15 | 2019 | 9 | |
| 16 | 2018 | 19 | |
| 17 | 2017 | 74 | |
| 18 | 2011 | 34 | |
| 19 | 2009 | 81 | |
| 20 | 2007 | 313 |
About Pradeep Kumar Das
Pradeep Kumar Das is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Neurology, Aging and Plant Science, having authored 68 papers that have together received 4.5k indexed citations. Recurring topics across this work include Plant Molecular Biology Research (18 papers), Plant Reproductive Biology (17 papers), Digital Imaging for Blood Diseases (16 papers), COVID-19 diagnosis using AI (14 papers), AI in cancer detection (13 papers), TGF-β signaling in diseases (6 papers), Brain Tumor Detection and Classification (5 papers) and Metal complexes synthesis and properties (5 papers). The work is most often cited by research in Aging (172 citations), Plant Science (2.4k citations), Molecular Biology (2.9k citations), Computer Vision and Pattern Recognition (541 citations) and Biophysics (148 citations). Pradeep Kumar Das has collaborated with scholars based in India, United States and France. Frequent co-authors include Sukadev Meher, Elliot M. Meyerowitz, Marcus G. Heisler, G. Venugopala Reddy, Carolyn Ohno, Richard W. Padgett, Patrick Sieber, Jeff A. Long, Adyasha Sahu and Vincent Mirabet. Their work appears in journals such as Development, Proceedings of the National Academy of Sciences, Engineering Applications of Artificial Intelligence, Biomedical Signal Processing and Control and Measurement.
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.