Bino John

11.9k total citations · 4 hit papers
26 papers, 9.2k citations indexed

About

Bino John is a scholar working on Molecular Biology, Cancer Research and Materials Chemistry. According to data from OpenAlex, Bino John has authored 26 papers receiving a total of 9.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 14 papers in Cancer Research and 4 papers in Materials Chemistry. Recurrent topics in Bino John's work include RNA modifications and cancer (13 papers), MicroRNA in disease regulation (10 papers) and RNA Research and Splicing (10 papers). Bino John is often cited by papers focused on RNA modifications and cancer (13 papers), MicroRNA in disease regulation (10 papers) and RNA Research and Splicing (10 papers). Bino John collaborates with scholars based in United States, United Kingdom and Germany. Bino John's co-authors include Debora S. Marks, Chris Sander, Thomas Tuschl, Anton J. Enright, Alexei A. Aravin, Ulrike Gaul, James J. Russo, Minchen Chien, Mihaela Zavolan and Sébastien Pfeffer and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Bino John

26 papers receiving 9.1k citations

Hit Papers

Human MicroRNA Targets 2003 2026 2010 2018 2004 2003 2004 2023 1000 2.0k 3.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bino John United States 20 7.2k 6.0k 793 670 483 26 9.2k
Jin‐Wu Nam South Korea 27 9.0k 1.2× 7.6k 1.3× 974 1.2× 450 0.7× 373 0.8× 58 11.1k
Janell M. Schelter United States 17 8.0k 1.1× 5.5k 0.9× 652 0.8× 410 0.6× 627 1.3× 20 9.5k
György Hutvàgner Australia 33 10.4k 1.4× 6.4k 1.1× 639 0.8× 1.7k 2.5× 272 0.6× 69 12.4k
Mariana Lagos‐Quintana United States 11 8.5k 1.2× 6.8k 1.1× 555 0.7× 1.6k 2.4× 361 0.7× 14 10.5k
Marc R. Fabian Canada 31 5.8k 0.8× 3.3k 0.5× 550 0.7× 714 1.1× 239 0.5× 58 7.4k
Stacia K. Wyman United States 25 9.6k 1.3× 5.9k 1.0× 671 0.8× 891 1.3× 424 0.9× 37 11.6k
Rosalind C. Lee United States 10 10.9k 1.5× 9.3k 1.5× 688 0.9× 1.4k 2.1× 287 0.6× 10 13.7k
Jinju Han South Korea 19 12.9k 1.8× 10.6k 1.8× 837 1.1× 1.3k 1.9× 311 0.6× 29 15.2k
Ana Kozomara United Kingdom 6 7.6k 1.0× 6.3k 1.0× 630 0.8× 1.9k 2.8× 194 0.4× 6 10.2k
Sebastian D. Mackowiak Germany 16 8.9k 1.2× 7.0k 1.2× 513 0.6× 685 1.0× 180 0.4× 23 10.3k

Countries citing papers authored by Bino John

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Bino John

This figure shows the co-authorship network connecting the top 25 collaborators of Bino John. A scholar is included among the top collaborators of Bino John based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bino John. Bino John is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Miljković, Filip, et al.. (2023). Interpretable bilinear attention network with domain adaptation improves drug–target prediction. Nature Machine Intelligence. 5(2). 126–136. 170 indexed citations breakdown →
2.
Sun, Zhaoyue, Jiazheng Li, Gabriele Pergola, et al.. (2022). PHEE: A Dataset for Pharmacovigilance Event Extraction from Text. 5571–5587. 9 indexed citations
3.
Miljković, Filip, et al.. (2021). Hierarchical Clustering Split for Low-Bias Evaluation of Drug-Target Interaction Prediction. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 641–644. 20 indexed citations
4.
Liu, Teresa T., Gustavo Arango-Argoty, Zhihua Li, et al.. (2015). Noncoding RNAs that associate with YB-1 alter proliferation in prostate cancer cells. RNA. 21(6). 1159–1172. 19 indexed citations
5.
Kim, Sang Woo, Elane Fishilevich, Gustavo Arango-Argoty, et al.. (2015). Genome-Wide Transcript Profiling Reveals Novel Breast Cancer-Associated Intronic Sense RNAs. PLoS ONE. 10(3). e0120296–e0120296. 2 indexed citations
6.
Liu, Teresa T., Zhihua Li, & Bino John. (2014). Enhanced Detection of Small RNAs Using a Nonradioactive Approach. Methods in molecular biology. 1173. 123–133. 2 indexed citations
7.
Li, Zhihua, Fatih Ozsolak, Sang Woo Kim, et al.. (2012). An in-depth map of polyadenylation sites in cancer. Nucleic Acids Research. 40(17). 8460–8471. 111 indexed citations
8.
Li, Zhihua, Christine Ender, Gunter Meister, et al.. (2012). Extensive terminal and asymmetric processing of small RNAs from rRNAs, snoRNAs, snRNAs, and tRNAs. Nucleic Acids Research. 40(14). 6787–6799. 264 indexed citations
9.
Ozsolak, Fatih, Philipp Kapranov, Sylvain Foissac, et al.. (2010). Comprehensive Polyadenylation Site Maps in Yeast and Human Reveal Pervasive Alternative Polyadenylation. Cell. 143(6). 1018–1029. 315 indexed citations
10.
Kim, Sang Woo, Zhihua Li, Patrick S. Moore, et al.. (2010). A sensitive non-radioactive northern blot method to detect small RNAs. Nucleic Acids Research. 38(7). e98–e98. 238 indexed citations
11.
Kapranov, Philipp, Fatih Ozsolak, Sang Woo Kim, et al.. (2010). New class of gene-termini-associated human RNAs suggests a novel RNA copying mechanism. Nature. 466(7306). 642–646. 75 indexed citations
12.
Lee, Ji, Zhihua Li, Rachel Brower–Sinning, & Bino John. (2007). Regulatory Circuit of Human MicroRNA Biogenesis. PLoS Computational Biology. 3(4). e67–e67. 61 indexed citations
13.
John, Bino, Chris Sander, & Debora S. Marks. (2006). Prediction of Human MicroRNA Targets. Humana Press eBooks. 342. 101–114. 119 indexed citations
14.
Chen, Po‐Yu, Krasimir Slanchev, Minchen Chien, et al.. (2005). The developmental miRNA profiles of zebrafish as determined by small RNA cloning. Genes & Development. 19(11). 1288–1293. 269 indexed citations
15.
John, Bino, Anton J. Enright, Alexei A. Aravin, et al.. (2004). Human MicroRNA Targets. PLoS Biology. 2(11). e363–e363. 3108 indexed citations breakdown →
16.
Pfeffer, Sébastien, Mihaela Zavolan, Friedrich A. Grässer, et al.. (2004). Identification of Virus-Encoded MicroRNAs. Science. 304(5671). 734–736. 1248 indexed citations breakdown →
17.
Topf, Maya, Matthew L. Baker, Bino John, Wah Chiu, & Andrej Săli. (2004). Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy. Journal of Structural Biology. 149(2). 191–203. 77 indexed citations
18.
Enright, Anton J., Bino John, Ulrike Gaul, et al.. (2003). MicroRNA targets in Drosophila. Genome biology. 5(1). R1–R1. 2832 indexed citations breakdown →
19.
John, Bino & Andrej Săli. (2003). Detection of homologous proteins by an intermediate sequence search. Protein Science. 13(1). 54–62. 22 indexed citations
20.
John, Bino, et al.. (2000). Depth-dependent analysis of membranes using benzophenone-based phospholipids. Biophysical Chemistry. 87(1). 37–42. 3 indexed citations

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.

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