W. Venus So

4.3k total citations · 3 hit papers
21 papers, 3.4k citations indexed

About

W. Venus So is a scholar working on Molecular Biology, Endocrine and Autonomic Systems and Computational Theory and Mathematics. According to data from OpenAlex, W. Venus So has authored 21 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Endocrine and Autonomic Systems and 6 papers in Computational Theory and Mathematics. Recurrent topics in W. Venus So's work include Computational Drug Discovery Methods (6 papers), Circadian rhythm and melatonin (6 papers) and Light effects on plants (4 papers). W. Venus So is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Circadian rhythm and melatonin (6 papers) and Light effects on plants (4 papers). W. Venus So collaborates with scholars based in United States, Switzerland and United Kingdom. W. Venus So's co-authors include Michael Rosbash, Jeffrey C. Hall, Maki Kaneko, Patrick Emery, Ravi Allada, Joan E. Rutila, Vipin Suri, Lea Sarov‐Blat, Li Liu and Jerrold M. Olefsky and has published in prestigious journals such as Nature, Cell and Nature Genetics.

In The Last Decade

W. Venus So

21 papers receiving 3.4k citations

Hit Papers

CRY, a Drosophila Clock and Light-Regulated Cryptochrome,... 1998 2026 2007 2016 1998 1998 1998 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
W. Venus So United States 12 1.9k 1.4k 1.3k 955 586 21 3.4k
Stephan Schneuwly Germany 32 609 0.3× 1.9k 1.4× 485 0.4× 2.3k 2.4× 481 0.8× 52 4.0k
Adrian Rothenfluh United States 21 2.1k 1.1× 1.4k 1.0× 1.5k 1.1× 707 0.7× 235 0.4× 44 3.2k
Lino Sáez United States 26 2.5k 1.3× 1.2k 0.9× 1.7k 1.3× 940 1.0× 236 0.4× 36 3.5k
Karen Wager‐Smith United States 16 1.9k 1.0× 1.3k 1.0× 1.4k 1.0× 839 0.9× 413 0.7× 19 3.3k
Eric Erquan Zhang China 29 2.5k 1.4× 712 0.5× 914 0.7× 1.5k 1.5× 212 0.4× 67 4.5k
Masashi Takao Japan 30 1.1k 0.6× 707 0.5× 1000 0.7× 2.5k 2.6× 243 0.4× 48 4.0k
Michael R. Koelle United States 27 927 0.5× 1.2k 0.9× 167 0.1× 1.6k 1.7× 402 0.7× 44 3.4k
Nicholas Gekakis United States 17 2.7k 1.4× 852 0.6× 1.3k 0.9× 880 0.9× 344 0.6× 21 3.7k
Sriram Sathyanarayanan United States 17 2.6k 1.4× 836 0.6× 1.3k 1.0× 803 0.8× 107 0.2× 32 3.6k

Countries citing papers authored by W. Venus So

Since Specialization
Citations

This map shows the geographic impact of W. Venus So'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 W. Venus So with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites W. Venus So more than expected).

Fields of papers citing papers by W. Venus So

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by W. Venus So. 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 W. Venus So. The network helps show where W. Venus So may publish in the future.

Co-authorship network of co-authors of W. Venus So

This figure shows the co-authorship network connecting the top 25 collaborators of W. Venus So. A scholar is included among the top collaborators of W. Venus So 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 W. Venus So. W. Venus So 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.
Rodriguez‐Esteban, Raul, et al.. (2022). Prediction of standard cell types and functional markers from textual descriptions of flow cytometry gating definitions using machine learning. Cytometry Part B Clinical Cytometry. 102(3). 220–227. 1 indexed citations
2.
Becker, Tim, Janick Weberpals, W. Venus So, et al.. (2020). An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort. Annals of Oncology. 31(11). 1561–1568. 18 indexed citations
3.
So, W. Venus, Tai-Hsien Ou Yang, Xing Yang, & Jianguo Zhi. (2018). Lack of UGT polymorphism association with idasanutlin pharmacokinetics in solid tumor patients. Cancer Chemotherapy and Pharmacology. 83(1). 209–213. 2 indexed citations
4.
Peng, Zhengwei, Paul Gillespie, Martin Weisel, et al.. (2013). A Crowd‐Based Process and Tool for HTS Hit Triage. Molecular Informatics. 32(4). 337–345. 4 indexed citations
5.
Weisel, Martin, et al.. (2012). PROLIX: Rapid Mining of Protein–Ligand Interactions in Large Crystal Structure Databases. Journal of Chemical Information and Modeling. 52(6). 1450–1461. 20 indexed citations
6.
Deng, Wei, Eric Scott, Steven J. Berthel, & W. Venus So. (2012). Deconvoluting complex patent Markush structures: A novel R-group numbering system. World Patent Information. 34(2). 128–133. 3 indexed citations
7.
Deng, Wei, Steven J. Berthel, & W. Venus So. (2011). Intuitive Patent Markush Structure Visualization Tool for Medicinal Chemists. Journal of Chemical Information and Modeling. 51(3). 511–520. 9 indexed citations
8.
Wei, Xin, Ann F. Hoffman, Shannon Hamilton, et al.. (2011). A simple statistical test to infer the causality of target/phenotype correlation from small molecule phenotypic screens. Bioinformatics. 28(3). 301–305. 3 indexed citations
9.
Tanrıkulu, Yusuf, et al.. (2010). Missing Value Estimation for Compound‐Target Activity Data. Molecular Informatics. 29(10). 678–684. 8 indexed citations
10.
Yang, Xiaoyong, Pat P. Ongusaha, Philip D.G. Miles, et al.. (2008). Phosphoinositide signalling links O-GlcNAc transferase to insulin resistance. Nature. 451(7181). 964–969. 484 indexed citations
11.
Marshall, Jan D., Gayle B. Collin, Sebastian Beck, et al.. (2007). Spectrum ofALMS1variants and evaluation of genotype-phenotype correlations in Alström syndrome. Human Mutation. 28(11). 1114–1123. 110 indexed citations
12.
Wu, Xuxia, Jelai Wang, Xiangqin Cui, et al.. (2007). The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine. 31(1). 5–17. 61 indexed citations
13.
Wu, Xuxia, Jelai Wang, Xiangqin Cui, et al.. (2007). The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine. 32(3). 356–356. 3 indexed citations
14.
Collin, Gayle B., Jan D. Marshall, Akihiro Ikeda, et al.. (2002). Mutations in ALMS1 cause obesity, type 2 diabetes and neurosensory degeneration in Alström syndrome. Nature Genetics. 31(1). 74–78. 277 indexed citations
15.
So, W. Venus, et al.. (2000). takeout, a Novel DrosophilaGene under Circadian Clock Transcriptional Regulation. Molecular and Cellular Biology. 20(18). 6935–6944. 116 indexed citations
16.
Sarov‐Blat, Lea, W. Venus So, Li Liu, & Michael Rosbash. (2000). The Drosophila takeout Gene Is a Novel Molecular Link between Circadian Rhythms and Feeding Behavior. Cell. 101(6). 647–656. 211 indexed citations
17.
Rutila, Joan E., et al.. (1998). CYCLE Is a Second bHLH-PAS Clock Protein Essential for Circadian Rhythmicity and Transcription of Drosophila period and timeless. Cell. 93(5). 805–814. 538 indexed citations breakdown →
18.
Emery, Patrick, W. Venus So, Maki Kaneko, Jeffrey C. Hall, & Michael Rosbash. (1998). CRY, a Drosophila Clock and Light-Regulated Cryptochrome, Is a Major Contributor to Circadian Rhythm Resetting and Photosensitivity. Cell. 95(5). 669–679. 718 indexed citations breakdown →
19.
Allada, Ravi, et al.. (1998). A Mutant Drosophila Homolog of Mammalian Clock Disrupts Circadian Rhythms and Transcription of period and timeless. Cell. 93(5). 791–804. 608 indexed citations breakdown →
20.
So, W. Venus. (1997). Post-transcriptional regulation contributes to Drosophila clock gene mRNA cycling. The EMBO Journal. 16(23). 7146–7155. 221 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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