Eric Wen Su

1.6k total citations · 1 hit paper
20 papers, 1.2k citations indexed

About

Eric Wen Su is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Eric Wen Su has authored 20 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 5 papers in Oncology and 4 papers in Cancer Research. Recurrent topics in Eric Wen Su's work include Gene expression and cancer classification (7 papers), Bioinformatics and Genomic Networks (5 papers) and Biomedical Text Mining and Ontologies (3 papers). Eric Wen Su is often cited by papers focused on Gene expression and cancer classification (7 papers), Bioinformatics and Genomic Networks (5 papers) and Biomedical Text Mining and Ontologies (3 papers). Eric Wen Su collaborates with scholars based in United States, Israel and Spain. Eric Wen Su's co-authors include Vladimir N. Uversky, Jiangang Liu, Christopher J. Oldfield, A. Keith Dunker, Narayanan B. Perumal, Jian Wang, Sheng-Bin Peng, Donald C. Paul, S Kovacevic and James Schrementi and has published in prestigious journals such as Journal of Clinical Oncology, Diabetes Care and Biochemistry.

In The Last Decade

Eric Wen Su

19 papers receiving 1.2k citations

Hit Papers

Intrinsic Disorder in Transcription Factors 2006 2026 2012 2019 2006 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eric Wen Su United States 12 779 230 151 138 106 20 1.2k
Gabriel Birrane United States 23 1.0k 1.3× 129 0.6× 105 0.7× 172 1.2× 152 1.4× 45 1.6k
Yukimasa Shiotsu Japan 22 1.1k 1.5× 305 1.3× 235 1.6× 241 1.7× 112 1.1× 47 1.8k
Jani Saarela Finland 19 583 0.7× 163 0.7× 113 0.7× 80 0.6× 97 0.9× 45 999
Zamal Ahmed United States 20 852 1.1× 201 0.9× 125 0.8× 70 0.5× 94 0.9× 41 1.1k
Giovanni Paolella Italy 22 1.0k 1.3× 121 0.5× 123 0.8× 186 1.3× 147 1.4× 49 1.6k
Marijane Russell United States 19 1.2k 1.5× 207 0.9× 152 1.0× 86 0.6× 87 0.8× 26 1.6k
Brandon T. Schurter United States 9 1.9k 2.4× 274 1.2× 192 1.3× 299 2.2× 86 0.8× 10 2.2k
Daniel Hirschberg Sweden 17 724 0.9× 114 0.5× 86 0.6× 47 0.3× 99 0.9× 21 1.1k
Tetsuro Orita Japan 14 1.0k 1.3× 372 1.6× 178 1.2× 175 1.3× 107 1.0× 17 1.3k
Punit Saraon Canada 19 805 1.0× 236 1.0× 152 1.0× 128 0.9× 267 2.5× 25 1.5k

Countries citing papers authored by Eric Wen Su

Since Specialization
Citations

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

Fields of papers citing papers by Eric Wen Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric Wen Su. 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 Eric Wen Su. The network helps show where Eric Wen Su may publish in the future.

Co-authorship network of co-authors of Eric Wen Su

This figure shows the co-authorship network connecting the top 25 collaborators of Eric Wen Su. A scholar is included among the top collaborators of Eric Wen Su 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 Eric Wen Su. Eric Wen Su 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.
Bruno, Débora S., et al.. (2022). Disparities in Biomarker Testing and Clinical Trial Enrollment Among Patients With Lung, Breast, or Colorectal Cancers in the United States. JCO Precision Oncology. 6(6). e2100427–e2100427. 44 indexed citations
2.
Hess, Lisa M., et al.. (2021). Racial disparities in comprehensive biomarker testing and clinical trial enrollment among patients with metastatic colorectal cancer (mCRC).. Journal of Clinical Oncology. 39(28_suppl). 125–125. 1 indexed citations
3.
Bruno, Débora S., et al.. (2021). Racial disparities in biomarker testing and clinical trial enrollment in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology. 39(15_suppl). 9005–9005. 34 indexed citations
4.
Su, Eric Wen. (2018). Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov. Methods in molecular biology. 1903. 61–72. 1 indexed citations
5.
Su, Eric Wen & T.M. Sanger. (2017). Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov. PeerJ. 5. e3154–e3154. 20 indexed citations
6.
Raz, Itamar, Antonio Ceriello, Peter W.F. Wilson, et al.. (2011). Post Hoc Subgroup Analysis of the HEART2D Trial Demonstrates Lower Cardiovascular Risk in Older Patients Targeting Postprandial Versus Fasting/Premeal Glycemia. Diabetes Care. 34(7). 1511–1513. 58 indexed citations
7.
Campen, Andrew, Yuni Xia, Ying Guo, et al.. (2008). Mining Gene Expression Database for Primary Human Disease Tissues. 1604–1607.
8.
Schmidt, Robert J., Youyan Zhang, Yang Zhao, et al.. (2007). A Novel Splicing Variant of Proprotein Convertase Subtilisin/Kexin Type 9. DNA and Cell Biology. 27(4). 183–189. 16 indexed citations
9.
Xia, Yuni, Andrew Campen, Ying Guo, et al.. (2007). DGEM — A Microarray Gene Expression Database for Primary Human Disease Tissues. Molecular Diagnosis & Therapy. 11(3). 145–149. 7 indexed citations
10.
Feng, Xingdong, Shuguang Huang, Jianyong Shou, et al.. (2007). Analysis of Pathway Activity in Primary Tumors and NCI60 Cell Lines Using Gene Expression Profiling Data. Genomics Proteomics & Bioinformatics. 5(1). 15–24. 11 indexed citations
11.
Lan, Yu, Sheng-Bin Peng, James Schrementi, et al.. (2006). Identification and expression of novel isoforms of human stromal cell-derived factor 1. Gene. 374. 174–179. 170 indexed citations
13.
Wang, Huixia, Shuguang Huang, Jianyong Shou, et al.. (2006). Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data. BMC Genomics. 7(1). 166–166. 48 indexed citations
14.
Liu, Jiangang, Narayanan B. Perumal, Christopher J. Oldfield, et al.. (2006). Intrinsic Disorder in Transcription Factors. Biochemistry. 45(22). 6873–6888. 592 indexed citations breakdown →
15.
Calley, John, et al.. (2006). A Flexible Integration and Visualisation System for Biomarker Discovery. PubMed. 5(4). 219–223. 2 indexed citations
16.
Zhang, Youyan, Robert J. Schmidt, Patricia Foxworthy, et al.. (2005). Niacin mediates lipolysis in adipose tissue through its G-protein coupled receptor HM74A. Biochemical and Biophysical Research Communications. 334(2). 729–732. 65 indexed citations
17.
Fogel, Gary B., Dana Weekes, Gábor Varga, et al.. (2005). A statistical analysis of the TRANSFAC database. Biosystems. 81(2). 137–154. 34 indexed citations
18.
Lu, Deshun, Xiu‐Juan Yuan, Robert J. Evans, et al.. (2005). Cloning and functional characterization of the rabbit C-C chemokine receptor 2. BMC Immunology. 6(1). 15–15. 8 indexed citations
19.
Lahn, Michael, Su Chen, Shuyu Li, et al.. (2004). Expression Levels of Protein Kinase C-α in Non–Small-Cell Lung Cancer. Clinical Lung Cancer. 6(3). 184–189. 34 indexed citations
20.
Savkur, Rajesh S., Yifei Wu, Kelli Bramlett, et al.. (2003). Alternative splicing within the ligand binding domain of the human constitutive androstane receptor. Molecular Genetics and Metabolism. 80(1-2). 216–226. 45 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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