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
- Genomics and Chromatin Dynamics 2
- Ubiquitin and proteasome pathways 2
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- Advanced Graph Neural Networks 2
- Co-authors
- Caroline Badouel (2 shared papers)Helen McNeill (2 shared papers)Prantik Bhattacharyya (3 shared papers)Shyhtsun Felix Wu (1 shared paper)Nancy Amin (1 shared paper)Thierry Le Bihan (1 shared paper)Laura Gardano (1 shared paper)Sharmistha Sinha (8 shared papers)
- Journals
- International Journal of Biological Macromolecules (2 papers)Genetics (2 papers)Biophysical Journal (2 papers)Current Opinion in Cell Biology (1 paper)Social Network Analysis and Mining (1 paper)
- Partner nations
- IndiaCanadaUnited States
In The Last Decade
Ankush Garg
18 papers receiving 486 citations
Peers
Comparison fields: 5 of 87
- Cell Biology 242
- Statistical and Nonlinear Physics 61
- Molecular Biology 250
- Artificial Intelligence 65
- Information Systems 45
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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 177 | |
| 2 | 2009 | 90 | |
| 3 | 2010 | 88 | |
| 4 | 2018 | 22 | |
| 5 | 2021 | 20 | |
| 6 | 2019 | 19 | |
| 7 | 2023 | 19 | |
| 8 | 2006 | 15 | |
| 9 | 2009 | 11 | |
| 10 | 2018 | 8 | |
| 11 | 2004 | 7 | |
| 12 | 2022 | 5 | |
| 13 | 2024 | 3 | |
| 14 | 2014 | 3 | |
| 15 | 2021 | 3 | |
| 16 | 2002 | 1 | |
| 17 | 2021 | 1 | |
| 18 | 2024 | 1 | |
| 19 | 2009 | 1 | |
| 20 | 2022 | 0 |
About Ankush Garg
Ankush Garg is a scholar working on Molecular Biology, Artificial Intelligence, Materials Chemistry, Oncology and Surgery, having authored 21 papers that have together received 494 indexed citations. Recurring topics across this work include RNA Research and Splicing (4 papers), HIV Research and Treatment (2 papers), Genomics and Chromatin Dynamics (2 papers), Advanced Graph Neural Networks (2 papers), Complex Network Analysis Techniques (2 papers), Hippo pathway signaling and YAP/TAZ (2 papers), Cancer-related Molecular Pathways (2 papers) and Ubiquitin and proteasome pathways (2 papers). The work is most often cited by research in Cell Biology (242 citations), Statistical and Nonlinear Physics (61 citations), Molecular Biology (250 citations), Artificial Intelligence (65 citations) and Information Systems (45 citations). Ankush Garg has collaborated with scholars based in India, Canada and United States. Frequent co-authors include Caroline Badouel, Helen McNeill, Prantik Bhattacharyya, Shyhtsun Felix Wu, Nancy Amin, Thierry Le Bihan, Laura Gardano, Sharmistha Sinha, Malay K. Sannigrahi and Harpreet Kaur. Their work appears in journals such as International Journal of Biological Macromolecules, Genetics, Biophysical Journal, Current Opinion in Cell Biology and Social Network Analysis and Mining.
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.