Vidya Kamath
- Molecular Biology
- Oncology top 10%
- Cancer Research top 10%
- Pulmonary and Respiratory Medicine
- Pathology and Forensic Medicine top 10%
- Co-authors
- A RenukaSteven A. EschrichTimothy J. YeatmanDiego ArangoMike GruidlRachel KerrTorben F. ØrntoftMichael Christie
- Topics
- Gene expression and cancer classification (5 papers)Bioinformatics and Genomic Networks (4 papers)Advanced Neural Network Applications (3 papers)
- Partner nations
- United StatesIndiaSweden
In The Last Decade
Vidya Kamath
21 papers receiving 829 citations
Peers
Comparison fields: 5 of 96
- Molecular Biology 371
- Oncology 316
- Cancer Research 194
- Pulmonary and Respiratory Medicine 166
- Pathology and Forensic Medicine 150
Countries citing papers authored by Vidya Kamath
This map shows the geographic impact of Vidya Kamath'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 Vidya Kamath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vidya Kamath more than expected).
Fields of papers citing papers by Vidya Kamath
This network shows the impact of papers produced by Vidya Kamath. 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 Vidya Kamath. The network helps show where Vidya Kamath may publish in the future.
Co-authorship network of co-authors of Vidya Kamath
This figure shows the co-authorship network connecting the top 25 collaborators of Vidya Kamath. A scholar is included among the top collaborators of Vidya Kamath based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vidya Kamath. Vidya Kamath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | 66 | |
| 6 | 2 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 99 | |
| 10 | 145 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 118 | |
| 14 | Enhancing Gene Expression Signatures in Cancer Prediction Models: Understanding and Managing Classification Complexity | 1 |
| 15 | 343 | |
| 16 | 1 | |
| 17 | 4 | |
| 18 | 4 | |
| 19 | Use of random subspace ensembles on gene expression profi les to enhance the accuracy of survival prediction for colon cancer patients | 1 |
| 20 | 16 |
About Vidya Kamath
Vidya Kamath is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Industrial and Manufacturing Engineering, having authored 23 papers that have together received 843 indexed citations. Recurring topics across this work include Gene expression and cancer classification (5 papers), Bioinformatics and Genomic Networks (4 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Cancer Research (194 citations), Oncology (316 citations) and Pathology and Forensic Medicine (150 citations). Vidya Kamath has collaborated with scholars based in United States, India and Sweden. Frequent co-authors include A Renuka, Steven A. Eschrich, Timothy J. Yeatman, Diego Arango, Mike Gruidl, Rachel Kerr, Torben F. Ørntoft, Michael Christie, Robert N. Jorissen and Lauri A. Aaltonen. Their work appears in journals such as Journal of Clinical Investigation, Cancer Research and Clinical Cancer Research.
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.