Bino John
- Cancer Research top 0.1%
- MicroRNA in disease regulation 10
- Cancer-related molecular mechanisms research 9
- Molecular Biology top 0.5%
- RNA modifications and cancer 13
- RNA Research and Splicing 10
- RNA and protein synthesis mechanisms 4
- Protein Structure and Dynamics 2
- Immunology top 5%
- Aging top 5%
- Structural Biology top 5%
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- Computational Drug Discovery Methods 3
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- Machine Learning in Materials Science 2
- Co-authors
- Chris SanderDebora S. MarksThomas TuschlAnton J. EnrightAlexei A. AravinUlrike GaulJames J. RussoMinchen Chien
- Journals
- Nucleic Acids Research (3 papers)PLoS Biology (2 papers)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Bino John
26 papers receiving 9.1k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Cancer Research 6.0k
- Molecular Biology 7.2k
- Immunology 793
- Aging 60
- Structural Biology 46
Countries citing papers authored by Bino John
This map shows the geographic impact of Bino John'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 Bino John with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bino John more than expected).
Fields of papers citing papers by Bino John
This network shows the impact of papers produced by Bino John. 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 Bino John. The network helps show where Bino John may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Bino John, 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 | Interpretable bilinear attention network with domain adaptation improves drug–target predictionbreakdown → | 2023 | 170 |
| 2 | 2022 | 9 | |
| 3 | 2021 | 20 | |
| 4 | 2015 | 19 | |
| 5 | 2015 | 2 | |
| 6 | 2014 | 2 | |
| 7 | 2012 | 111 | |
| 8 | 2012 | 264 | |
| 9 | 2010 | 315 | |
| 10 | 2010 | 238 | |
| 11 | 2010 | 75 | |
| 12 | 2007 | 61 | |
| 13 | 2006 | 119 | |
| 14 | 2005 | 269 | |
| 15 | Human MicroRNA Targetsbreakdown → | 2004 | 3108 |
| 16 | 2004 | 77 | |
| 17 | Identification of Virus-Encoded MicroRNAsbreakdown → | 2004 | 1248 |
| 18 | MicroRNA targets in Drosophilabreakdown → | 2003 | 2832 |
| 19 | 2003 | 22 | |
| 20 | 2000 | 3 |
About Bino John
Bino John is a scholar working on Cancer Research, Structural Biology and Molecular Biology, having authored 26 papers that have together received 9.2k indexed citations. Recurring topics across this work include RNA modifications and cancer (13 papers), MicroRNA in disease regulation (10 papers), RNA Research and Splicing (10 papers), Cancer-related molecular mechanisms research (9 papers), RNA and protein synthesis mechanisms (4 papers), Computational Drug Discovery Methods (3 papers), Protein Structure and Dynamics (2 papers) and Machine Learning in Materials Science (2 papers). The work is most often cited by research in Cancer Research (6.0k citations), Molecular Biology (7.2k citations) and Immunology (793 citations). Bino John has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Chris Sander, Debora S. Marks, Thomas Tuschl, Anton J. Enright, Alexei A. Aravin, Ulrike Gaul, James J. Russo, Minchen Chien, Mihaela Zavolan and Sébastien Pfeffer. Their work appears in journals such as Nucleic Acids Research, PLoS Biology, Proceedings of the National Academy of Sciences, Journal of Structural Biology and RNA.
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.