Surendra Kumar Shukla
Impact in
- Cancer Research top 2%
- Cancer, Hypoxia, and Metabolism
- Cancer, Lipids, and Metabolism
- Oncology top 5%
- Pancreatic and Hepatic Oncology Research
Papers in
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- Cancer, Hypoxia, and Metabolism 17
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- RNA modifications and cancer 6
- Muscle Physiology and Disorders 5
- Metabolism, Diabetes, and Cancer 4
- Biochemical and Molecular Research 3
- Co-authors
- Pankaj K. Singh (26 shared papers)Fang Yu (8 shared papers)Venugopal Gunda (10 shared papers)Nina V. Chaika (12 shared papers)Vijay Kumar (4 shared papers)Ryan J. King (9 shared papers)Jaime Abrego (7 shared papers)Enza Vernucci (6 shared papers)
- Journals
- Cancer Letters (4 papers)Oncotarget (3 papers)eLife (2 papers)Cancer Cell (2 papers)Cancers (2 papers)
- Partner nations
- United StatesIndiaChina
In The Last Decade
Surendra Kumar Shukla
47 papers receiving 2.3k citations
Peers
Comparison fields: 5 of 105
- Cancer Research 790
- Oncology 590
- Geriatrics and Gerontology 76
- Molecular Biology 1.2k
- Physiology 314
Countries citing papers authored by Surendra Kumar Shukla
This map shows the geographic impact of Surendra Kumar Shukla'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 Surendra Kumar Shukla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Surendra Kumar Shukla more than expected).
Fields of papers citing papers by Surendra Kumar Shukla
This network shows the impact of papers produced by Surendra Kumar Shukla. 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 Surendra Kumar Shukla. The network helps show where Surendra Kumar Shukla may publish in the future.
Co-authors
The 25 scholars most cited alongside Surendra Kumar Shukla, 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 49 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 454 | |
| 2 | 2014 | 211 | |
| 3 | 2017 | 182 | |
| 4 | 2021 | 105 | |
| 5 | 2015 | 92 | |
| 6 | 2017 | 85 | |
| 7 | 2017 | 82 | |
| 8 | 2017 | 78 | |
| 9 | 2015 | 77 | |
| 10 | 2010 | 72 | |
| 11 | 2024 | 65 | |
| 12 | 2020 | 62 | |
| 13 | 2020 | 61 | |
| 14 | 2010 | 59 | |
| 15 | 2015 | 59 | |
| 16 | 2019 | 56 | |
| 17 | 2015 | 47 | |
| 18 | 2015 | 46 | |
| 19 | 2023 | 44 | |
| 20 | 2020 | 39 |
About Surendra Kumar Shukla
Surendra Kumar Shukla is a scholar working on Cancer Research, Molecular Biology, Oncology, Physiology and Biotechnology, having authored 49 papers that have together received 2.3k indexed citations. Recurring topics across this work include Cancer, Hypoxia, and Metabolism (17 papers), RNA modifications and cancer (6 papers), Pancreatic and Hepatic Oncology Research (6 papers), Muscle Physiology and Disorders (5 papers), Nutrition and Health in Aging (5 papers), Metabolism, Diabetes, and Cancer (4 papers), Biochemical and Molecular Research (3 papers) and GDF15 and Related Biomarkers (3 papers). The work is most often cited by research in Cancer Research (790 citations), Oncology (590 citations), Geriatrics and Gerontology (76 citations), Molecular Biology (1.2k citations) and Physiology (314 citations). Surendra Kumar Shukla has collaborated with scholars based in United States, India and China. Frequent co-authors include Pankaj K. Singh, Fang Yu, Venugopal Gunda, Nina V. Chaika, Vijay Kumar, Ryan J. King, Jaime Abrego, Enza Vernucci, Aneesha Dasgupta and Kamiya Mehla. Their work appears in journals such as Cancer Letters, Oncotarget, eLife, Cancer Cell and Cancers.
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.