John Tra
Impact in
- Spectroscopy top 5%
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
- Cell Biology top 10%
- Endoplasmic Reticulum Stress and Disease
Papers in
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- Advanced Proteomics Techniques and Applications 3
- Mass Spectrometry Techniques and Applications 1
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- RNA modifications and cancer 1
- Gene expression and cancer classification 1
- Machine Learning in Bioinformatics 1
- Protein purification and stability 1
- Viral Infectious Diseases and Gene Expression in Insects 1
- Co-authors
- Samir Hanash (5 shared papers)David E. Misek (3 shared papers)Franck Brichory (2 shared papers)Eric Puravs (2 shared papers)François Le Naour (1 shared paper)Bong Kyung Shin (1 shared paper)Rong Zhao (1 shared paper)Claire Michael (1 shared paper)
- Journals
- PROTEOMICS (2 papers)PROTEOMICS - CLINICAL APPLICATIONS (1 paper)Molecular & Cellular Proteomics (1 paper)Mechanisms of Ageing and Development (1 paper)Journal of Biological Chemistry (1 paper)
- Partner nations
- United States
In The Last Decade
John Tra
7 papers receiving 750 citations
Peers
Comparison fields: 5 of 91
- Spectroscopy 245
- Cell Biology 168
- Molecular Biology 544
- Aging 13
- Immunology 90
Countries citing papers authored by John Tra
This map shows the geographic impact of John Tra'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 John Tra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Tra more than expected).
Fields of papers citing papers by John Tra
This network shows the impact of papers produced by John Tra. 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 John Tra. The network helps show where John Tra may publish in the future.
Co-authors
The 25 scholars most cited alongside John Tra, 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 | 2003 | 460 | |
| 2 | 2005 | 119 | |
| 3 | 2001 | 67 | |
| 4 | 2005 | 60 | |
| 5 | 2002 | 43 | |
| 6 | 2014 | 30 | |
| 7 | 2011 | 6 |
About John Tra
John Tra is a scholar working on Spectroscopy, Molecular Biology, Oncology, Cell Biology and Cardiology and Cardiovascular Medicine, having authored 7 papers that have together received 785 indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (3 papers), RNA modifications and cancer (1 paper), Gene expression and cancer classification (1 paper), Machine Learning in Bioinformatics (1 paper), Mass Spectrometry Techniques and Applications (1 paper), Protein purification and stability (1 paper), Viral Infectious Diseases and Gene Expression in Insects (1 paper) and Clusterin in disease pathology (1 paper). The work is most often cited by research in Spectroscopy (245 citations), Cell Biology (168 citations), Molecular Biology (544 citations), Aging (13 citations) and Immunology (90 citations). John Tra has collaborated with scholars based in United States. Frequent co-authors include Samir Hanash, David E. Misek, Franck Brichory, Eric Puravs, François Le Naour, Bong Kyung Shin, Rong Zhao, Claire Michael, Jun Ho Jang and Hong Wang. Their work appears in journals such as PROTEOMICS, PROTEOMICS - CLINICAL APPLICATIONS, Molecular & Cellular Proteomics, Mechanisms of Ageing and Development and Journal of Biological Chemistry.
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.