Ankush Garg
Impact in
- Cell Biology top 5%
- Hippo pathway signaling and YAP/TAZ
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- Complex Network Analysis Techniques
Papers in
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- RNA Research and Splicing 4
- Ubiquitin and proteasome pathways 2
- Developmental Biology and Gene Regulation 2
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- Advanced Graph Neural Networks 2
- Co-authors
- Helen McNeill (2 shared papers)Caroline Badouel (2 shared papers)Prantik Bhattacharyya (4 shared papers)Shyhtsun Felix Wu (1 shared paper)Nancy Amin (1 shared paper)Laura Gardano (1 shared paper)Thierry Le Bihan (1 shared paper)Sharmistha Sinha (8 shared papers)
- Journals
- International Journal of Biological Macromolecules (2 papers)Genetics (2 papers)Biophysical Journal (2 papers)Social Network Analysis and Mining (1 paper)Developmental Cell (1 paper)
- Partner nations
- IndiaUnited StatesCanada
In The Last Decade
Ankush Garg
20 papers receiving 518 citations
Peers
Comparison fields: 5 of 87
- Cell Biology 248
- Statistical and Nonlinear Physics 68
- Molecular Biology 248
- Information Systems 51
- Artificial Intelligence 72
Countries citing papers authored by Ankush Garg
This map shows the geographic impact of Ankush Garg'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 Ankush Garg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankush Garg more than expected).
Fields of papers citing papers by Ankush Garg
This network shows the impact of papers produced by Ankush Garg. 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 Ankush Garg. The network helps show where Ankush Garg may publish in the future.
Co-authors
The 25 scholars most cited alongside Ankush Garg, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 182 | |
| 2 | 2010 | 97 | |
| 3 | 2009 | 93 | |
| 4 | 2018 | 22 | |
| 5 | 2019 | 21 | |
| 6 | 2023 | 20 | |
| 7 | 2021 | 20 | |
| 8 | 2006 | 17 | |
| 9 | 2018 | 13 | |
| 10 | 2009 | 13 | |
| 11 | 2004 | 7 | |
| 12 | 2022 | 5 | |
| 13 | 2009 | 4 | |
| 14 | 2024 | 3 | |
| 15 | 2021 | 3 | |
| 16 | 2014 | 3 | |
| 17 | 2024 | 2 | |
| 18 | 2022 | 1 | |
| 19 | 2002 | 1 | |
| 20 | 2009 | 1 |
About Ankush Garg
Ankush Garg is a scholar working on Molecular Biology, Artificial Intelligence, Materials Chemistry, Statistical and Nonlinear Physics and Oncology, having authored 22 papers that have together received 529 indexed citations. Recurring topics across this work include RNA Research and Splicing (4 papers), Complex Network Analysis Techniques (3 papers), Ubiquitin and proteasome pathways (2 papers), Cancer-related Molecular Pathways (2 papers), Advanced Graph Neural Networks (2 papers), Peer-to-Peer Network Technologies (2 papers), Developmental Biology and Gene Regulation (2 papers) and Hippo pathway signaling and YAP/TAZ (2 papers). The work is most often cited by research in Cell Biology (248 citations), Statistical and Nonlinear Physics (68 citations), Molecular Biology (248 citations), Information Systems (51 citations) and Artificial Intelligence (72 citations). Ankush Garg has collaborated with scholars based in India, United States and Canada. Frequent co-authors include Helen McNeill, Caroline Badouel, Prantik Bhattacharyya, Shyhtsun Felix Wu, Nancy Amin, Laura Gardano, Thierry Le Bihan, Sharmistha Sinha, Malay K. Sannigrahi and Harpreet Kaur. Their work appears in journals such as International Journal of Biological Macromolecules, Genetics, Biophysical Journal, Social Network Analysis and Mining and Developmental Cell.
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.