Neil Kumar
Impact in
- Spectroscopy top 10%
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
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- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Glycosylation and Glycoproteins Research
Papers in ⓘ
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- Cell Adhesion Molecules Research 2
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- Computational Drug Discovery Methods 2
- Co-authors
- Douglas A. Lauffenburger (6 shared papers)Alejandro Wolf‐Yadlin (2 shared papers)Forest M. White (2 shared papers)Hyung‐Do Kim (3 shared papers)Muhammad H. Zaman (2 shared papers)Sampsa Hautaniemi (1 shared paper)Yi Zhang (1 shared paper)Viara Grantcharova (1 shared paper)
- Journals
- PLoS Computational Biology (1 paper)Drug Discovery Today (1 paper)Sensors (1 paper)Molecular Pharmacology (1 paper)Biophysical Journal (1 paper)
- Partner nations
- United StatesBrazil
In The Last Decade
Neil Kumar
8 papers receiving 448 citations
Peers
Comparison fields: 5 of 75
- Spectroscopy 92
- Molecular Biology 333
- Computational Theory and Mathematics 75
- Oncology 123
- Radiology, Nuclear Medicine and Imaging 70
Countries citing papers authored by Neil Kumar
This map shows the geographic impact of Neil Kumar'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 Neil Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neil Kumar more than expected).
Fields of papers citing papers by Neil Kumar
This network shows the impact of papers produced by Neil Kumar. 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 Neil Kumar. The network helps show where Neil Kumar may publish in the future.
Co-authors
The 17 scholars most cited alongside Neil Kumar, 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 | 2006 | 190 | |
| 2 | 2006 | 91 | |
| 3 | 2006 | 91 | |
| 4 | 2008 | 48 | |
| 5 | 2007 | 22 | |
| 6 | 2006 | 20 | |
| 7 | 2024 | 3 | |
| 8 | 2023 | 1 |
About Neil Kumar
Neil Kumar is a scholar working on Immunology and Allergy, Computational Theory and Mathematics, Neurology, Media Technology and Oncology, having authored 8 papers that have together received 466 indexed citations. Recurring topics across this work include HER2/EGFR in Cancer Research (3 papers), Computational Drug Discovery Methods (2 papers), Protein Kinase Regulation and GTPase Signaling (2 papers), Cell Adhesion Molecules Research (2 papers), PI3K/AKT/mTOR signaling in cancer (2 papers), Long-Term Effects of COVID-19 (1 paper), Metabolism, Diabetes, and Cancer (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Spectroscopy (92 citations), Molecular Biology (333 citations), Computational Theory and Mathematics (75 citations), Oncology (123 citations) and Radiology, Nuclear Medicine and Imaging (70 citations). Neil Kumar has collaborated with scholars based in United States and Brazil. Frequent co-authors include Douglas A. Lauffenburger, Alejandro Wolf‐Yadlin, Forest M. White, Hyung‐Do Kim, Muhammad H. Zaman, Sampsa Hautaniemi, Yi Zhang, Viara Grantcharova, David de Graaf and Bart S. Hendriks. Their work appears in journals such as PLoS Computational Biology, Drug Discovery Today, Sensors, Molecular Pharmacology and Biophysical Journal.
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.