Selvakumar Kamatchinathan
- Molecular Biology top 2%
- Genomics and Phylogenetic Studies 2
- Machine Learning in Bioinformatics 1
- Genetics, Bioinformatics, and Biomedical Research 1
- Cell Biology top 5%
- Spectroscopy top 2%
- Advanced Proteomics Techniques and Applications 2
- Aging top 5%
- Immunology top 5%
-
- Scientific Computing and Data Management 2
-
- Genomics and Rare Diseases 1
-
- Research Data Management Practices 1
-
- Computational Physics and Python Applications 1
- Co-authors
- Juan Antonio VizcaínoDeepti J KunduYasset Pérez‐RiverolChakradhar BandlaSuresh HewapathiranaMathias WalzerShengbo WangJingwen Bai
- Journals
- Nucleic Acids Research (3 papers)PROTEOMICS (1 paper)The Journal of Open Source Software (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Selvakumar Kamatchinathan
3 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Molecular Biology 2.7k
- Cell Biology 451
- Spectroscopy 413
- Aging 43
- Immunology 472
Countries citing papers authored by Selvakumar Kamatchinathan
This map shows the geographic impact of Selvakumar Kamatchinathan'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 Selvakumar Kamatchinathan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Selvakumar Kamatchinathan more than expected).
Fields of papers citing papers by Selvakumar Kamatchinathan
This network shows the impact of papers produced by Selvakumar Kamatchinathan. 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 Selvakumar Kamatchinathan. The network helps show where Selvakumar Kamatchinathan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Selvakumar Kamatchinathan, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | The PRIDE database at 20 years: 2025 updatebreakdown → | 2024 | 375 |
| 4 | 2024 | 8 | |
| 5 | The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidencesbreakdown → | 2021 | 3918 |
About Selvakumar Kamatchinathan
Selvakumar Kamatchinathan is a scholar working on Information Systems and Management, Spectroscopy and Information Systems, having authored 5 papers that have together received 4.3k indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (2 papers), Genomics and Phylogenetic Studies (2 papers), Scientific Computing and Data Management (2 papers), Genomics and Rare Diseases (1 paper), Machine Learning in Bioinformatics (1 paper), Research Data Management Practices (1 paper), Computational Physics and Python Applications (1 paper) and Genetics, Bioinformatics, and Biomedical Research (1 paper). The work is most often cited by research in Molecular Biology (2.7k citations), Cell Biology (451 citations) and Spectroscopy (413 citations). Selvakumar Kamatchinathan has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Juan Antonio Vizcaíno, Deepti J Kundu, Yasset Pérez‐Riverol, Chakradhar Bandla, Suresh Hewapathirana, Mathias Walzer, Shengbo Wang, Jingwen Bai, Ananth Prakash and David García‐Seisdedos. Their work appears in journals such as Nucleic Acids Research, PROTEOMICS and The Journal of Open Source Software.
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.