Vanathi Gopalakrishnan
- Neurology top 10%
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- Machine Learning in Bioinformatics 18
- Gene expression and cancer classification 15
- Bioinformatics and Genomic Networks 10
- Biomedical Text Mining and Ontologies 7
- Protein Structure and Dynamics 5
- Artificial Intelligence top 10%
- Machine Learning and Data Classification 6
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- Advanced Proteomics Techniques and Applications 6
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- Data Mining Algorithms and Applications 5
Vanathi Gopalakrishnan
48 papers receiving 976 citations
Peers
Comparison fields: 5 of 148
- Neurology 170
- Health Informatics 10
- Molecular Biology 455
- Artificial Intelligence 191
- Genetics 58
Countries citing papers authored by Vanathi Gopalakrishnan
This map shows the geographic impact of Vanathi Gopalakrishnan'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 Vanathi Gopalakrishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vanathi Gopalakrishnan more than expected).
Fields of papers citing papers by Vanathi Gopalakrishnan
This network shows the impact of papers produced by Vanathi Gopalakrishnan. 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 Vanathi Gopalakrishnan. The network helps show where Vanathi Gopalakrishnan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Vanathi Gopalakrishnan, 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 | 2020 | 4 | |
| 2 | 2018 | 27 | |
| 3 | 2018 | 1 | |
| 4 | 2016 | 14 | |
| 5 | 2015 | 7 | |
| 6 | A novel framework to enhance scientific knowledge of cardiovascular MRI biomarkers and their application to pediatric cardiomyopathy classification | 2014 | 2 |
| 7 | 2014 | 16 | |
| 8 | 2012 | 67 | |
| 9 | 2012 | 8 | |
| 10 | 2011 | 42 | |
| 11 | 2011 | 65 | |
| 12 | 2011 | 21 | |
| 13 | 2011 | 28 | |
| 14 | 2010 | 90 | |
| 15 | 2009 | 6 | |
| 16 | An Evaluation of Discretization Methods for Learning Rules from Biomedical Datasets. | 2008 | 5 |
| 17 | 2005 | 172 | |
| 18 | 2004 | 21 | |
| 19 | 2004 | 8 | |
| 20 | The crystallographer's assistant | 1994 | 1 |
About Vanathi Gopalakrishnan
Vanathi Gopalakrishnan is a scholar working on Molecular Biology, Artificial Intelligence, Spectroscopy, Information Systems and Management and Information Systems, having authored 52 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (18 papers), Gene expression and cancer classification (15 papers), Bioinformatics and Genomic Networks (10 papers), Biomedical Text Mining and Ontologies (7 papers), Advanced Proteomics Techniques and Applications (6 papers), Machine Learning and Data Classification (6 papers), Protein Structure and Dynamics (5 papers) and Data Mining Algorithms and Applications (5 papers). The work is most often cited by research in Neurology (170 citations), Health Informatics (10 citations), Molecular Biology (455 citations), Artificial Intelligence (191 citations) and Genetics (58 citations). Vanathi Gopalakrishnan has collaborated with scholars based in United States, Netherlands and Mexico. Frequent co-authors include Jonathan L. Lustgarten, Shyam Visweswaran, Robert Bowser, Merit Cudkowicz, David Lacomis, Brian Liu, Himanshu Grover, William L. Bigbee, Gregory F. Cooper and Robert H. Brown. Their work appears in journals such as BMC Bioinformatics, Journal of Thoracic Oncology, Bioinformatics, Journal of Biomedical Informatics and Journal of Computational Biology.
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.