Vidhya G. Krishnan
Impact in
- Genetics top 5%
- Genomics and Rare Diseases
- Molecular Biology top 10%
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- Mitochondrial Function and Pathology
Papers in
-
- RNA and protein synthesis mechanisms 4
- Genomics and Phylogenetic Studies 2
- Machine Learning in Bioinformatics 1
- Genetics 5
- Genomics and Rare Diseases 3
- Diabetes and associated disorders 2
- Co-authors
- Sean D. Mooney (3 shared papers)Biao Li (2 shared papers)Matthew Mort (2 shared papers)Predrag Radivojac (2 shared papers)D.N. Cooper (2 shared papers)Fuxiao Xin (1 shared paper)David R. Westhead (1 shared paper)Uday S. Evani (2 shared papers)
- Journals
- iScience (2 papers)Bioinformatics (2 papers)Human Mutation (1 paper)BMC Biology (1 paper)Cell Death and Disease (1 paper)
- Partner nations
- SingaporeUnited KingdomUnited States
In The Last Decade
Vidhya G. Krishnan
10 papers receiving 982 citations
Vidhya G. Krishnan's Hit Papers
Peers
Comparison fields: 5 of 100
- Genetics 411
- Molecular Biology 720
- Clinical Biochemistry 58
- Cancer Research 113
- Aging 11
Countries citing papers authored by Vidhya G. Krishnan
This map shows the geographic impact of Vidhya G. Krishnan'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 Vidhya G. Krishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vidhya G. Krishnan more than expected).
Fields of papers citing papers by Vidhya G. Krishnan
This network shows the impact of papers produced by Vidhya G. Krishnan. 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 Vidhya G. Krishnan. The network helps show where Vidhya G. Krishnan may publish in the future.
Co-authors
The 25 scholars most cited alongside Vidhya G. Krishnan, 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 | Automated inference of molecular mechanisms of disease from amino acid substitutions Hit paper breakdown → | 2009 | 623 |
| 2 | 2003 | 113 | |
| 3 | 2010 | 51 | |
| 4 | 2020 | 47 | |
| 5 | 2021 | 46 | |
| 6 | 2010 | 44 | |
| 7 | 2019 | 42 | |
| 8 | 2021 | 21 | |
| 9 | 2012 | 6 | |
| 10 | 2023 | 5 | |
| 11 | 2016 | 1 | |
| 12 | 2016 | 0 |
About Vidhya G. Krishnan
Vidhya G. Krishnan is a scholar working on Molecular Biology, Genetics, Surgery, Epidemiology and Endocrinology, Diabetes and Metabolism, having authored 12 papers that have together received 999 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (4 papers), Pancreatic function and diabetes (3 papers), Genomics and Rare Diseases (3 papers), Genomics and Phylogenetic Studies (2 papers), Diabetes and associated disorders (2 papers), Machine Learning in Bioinformatics (1 paper), Cancer Genomics and Diagnostics (1 paper) and Diabetes Management and Research (1 paper). The work is most often cited by research in Genetics (411 citations), Molecular Biology (720 citations), Clinical Biochemistry (58 citations), Cancer Research (113 citations) and Aging (11 citations). Vidhya G. Krishnan has collaborated with scholars based in Singapore, United Kingdom and United States. Frequent co-authors include Sean D. Mooney, Biao Li, Matthew Mort, Predrag Radivojac, D.N. Cooper, Fuxiao Xin, David R. Westhead, Uday S. Evani, Shawn Hoon and Natasha Hui Jin Ng. Their work appears in journals such as iScience, Bioinformatics, Human Mutation, BMC Biology and Cell Death and Disease.
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.