Parit Bansal
Impact in
- Molecular Medicine top 10%
- Antibiotic Resistance in Bacteria
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- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- Microbial Metabolic Engineering and Bioproduction
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
Papers in
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- Cell Image Analysis Techniques 2
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- Microbial Metabolic Engineering and Bioproduction 4
- Bioinformatics and Genomic Networks 2
- Genomics and Phylogenetic Studies 2
- Machine Learning in Bioinformatics 1
- Co-authors
- Alan BridgeIoannis XénariosSylvain PouxEmmanuel BoutetLydie BougueleretDamien LieberherrMichel SchneiderMichael Tognolli
- Journals
- Nucleic Acids Research (1 paper)Database (1 paper)Bioinformatics (1 paper)Metabolites (1 paper)Methods in molecular biology (1 paper)
- Partner nations
- SwitzerlandUnited Kingdom
In The Last Decade
Parit Bansal
6 papers receiving 786 citations
Hit Papers
Peers
Comparison fields: 5 of 95
- Molecular Medicine 57
- Molecular Biology 593
- Computational Theory and Mathematics 90
- Endocrinology 29
- Ecology 74
Countries citing papers authored by Parit Bansal
This map shows the geographic impact of Parit Bansal'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 Parit Bansal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parit Bansal more than expected).
Fields of papers citing papers by Parit Bansal
This network shows the impact of papers produced by Parit Bansal. 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 Parit Bansal. The network helps show where Parit Bansal may publish in the future.
Co-authors
The 25 scholars most cited alongside Parit Bansal, 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 | 2022 | 7 | |
| 2 | 2021 | 126 | |
| 3 | 2021 | 3 | |
| 4 | 2020 | 2 | |
| 5 | 2019 | 69 | |
| 6 | UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View Hit paper breakdown → | 2015 | 581 |
About Parit Bansal
Parit Bansal is a scholar working on Biophysics, Molecular Biology, Spectroscopy, Pharmacology and Artificial Intelligence, having authored 6 papers that have together received 788 indexed citations. Recurring topics across this work include Microbial Metabolic Engineering and Bioproduction (4 papers), Bioinformatics and Genomic Networks (2 papers), Genomics and Phylogenetic Studies (2 papers), Cell Image Analysis Techniques (2 papers), AI in cancer detection (1 paper), Machine Learning in Bioinformatics (1 paper), Microbial Natural Products and Biosynthesis (1 paper) and Advanced Proteomics Techniques and Applications (1 paper). The work is most often cited by research in Molecular Medicine (57 citations), Molecular Biology (593 citations), Computational Theory and Mathematics (90 citations), Endocrinology (29 citations) and Ecology (74 citations). Parit Bansal has collaborated with scholars based in Switzerland and United Kingdom. Frequent co-authors include Alan Bridge, Ioannis Xénarios, Sylvain Poux, Emmanuel Boutet, Lydie Bougueleret, Damien Lieberherr, Michel Schneider, Michael Tognolli, Monica Pozzato and Nicole Redaschi. Their work appears in journals such as Nucleic Acids Research, Database, Bioinformatics, Metabolites and Methods in molecular 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.