Scott Dudek

4.0k total citations
52 papers, 2.2k citations indexed

About

Scott Dudek is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Scott Dudek has authored 52 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 32 papers in Genetics and 6 papers in Artificial Intelligence. Recurrent topics in Scott Dudek's work include Genetic Associations and Epidemiology (26 papers), Bioinformatics and Genomic Networks (26 papers) and Gene expression and cancer classification (16 papers). Scott Dudek is often cited by papers focused on Genetic Associations and Epidemiology (26 papers), Bioinformatics and Genomic Networks (26 papers) and Gene expression and cancer classification (16 papers). Scott Dudek collaborates with scholars based in United States, Belgium and United Kingdom. Scott Dudek's co-authors include Marylyn D. Ritchie, Sarah A. Pendergrass, William S. Bush, Yuki Bradford, Paul E. Hardin, Jerry H. Houl, Dan M. Roden, Ute I. Schwarz, Richard B. Kim and C. Michael Stein and has published in prestigious journals such as New England Journal of Medicine, Nature Communications and Neuron.

In The Last Decade

Scott Dudek

51 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Scott Dudek United States 24 979 852 359 294 263 52 2.2k
Margaret G. Ehm United States 25 1.2k 1.3× 1.7k 2.0× 128 0.4× 116 0.4× 193 0.7× 43 3.3k
Hélène Blanché France 25 1.1k 1.2× 639 0.8× 424 1.2× 147 0.5× 70 0.3× 61 3.2k
Tomas Axelsson Sweden 25 636 0.6× 522 0.6× 259 0.7× 133 0.5× 36 0.1× 69 2.0k
Tarif Awad United States 13 628 0.6× 188 0.2× 82 0.2× 337 1.1× 133 0.5× 15 1.3k
Ron Korstanje United States 32 1.3k 1.3× 998 1.2× 201 0.6× 42 0.1× 90 0.3× 114 3.3k
Jeffrey R. O’Connell United States 29 1.0k 1.0× 1.5k 1.7× 354 1.0× 59 0.2× 116 0.4× 86 3.4k
Albert V. Smith United States 22 1.3k 1.3× 733 0.9× 209 0.6× 28 0.1× 61 0.2× 59 2.4k
Ralph McGinnis United Kingdom 14 1.1k 1.2× 2.1k 2.5× 109 0.3× 980 3.3× 67 0.3× 25 4.5k
Asher Kohn Israel 4 2.5k 2.5× 614 0.7× 154 0.4× 518 1.8× 28 0.1× 5 4.3k
Yaron Mazor Israel 7 2.1k 2.1× 475 0.6× 128 0.4× 478 1.6× 25 0.1× 7 3.7k

Countries citing papers authored by Scott Dudek

Since Specialization
Citations

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

Fields of papers citing papers by Scott Dudek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott Dudek. 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 Scott Dudek. The network helps show where Scott Dudek may publish in the future.

Co-authorship network of co-authors of Scott Dudek

This figure shows the co-authorship network connecting the top 25 collaborators of Scott Dudek. A scholar is included among the top collaborators of Scott Dudek 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 Scott Dudek. Scott Dudek 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.
Hui, Daniel, Scott Dudek, Krzysztof Kiryluk, et al.. (2023). Risk factors affecting polygenic score performance across diverse cohorts. eLife. 12.
2.
Compher, Charlene, Ryan Quinn, Richard P. Haslam, et al.. (2023). Penn Healthy Diet survey: pilot validation and scoring. British Journal Of Nutrition. 131(1). 156–162. 1 indexed citations
3.
Singhal, Pankhuri, Yogasudha Veturi, Scott Dudek, et al.. (2023). Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets. The American Journal of Human Genetics. 110(4). 575–591. 9 indexed citations
4.
Li, Binglan, Katrin Sangkuhl, Ryan Whaley, et al.. (2022). How to Run the Pharmacogenomics Clinical Annotation Tool (PharmCAT). Clinical Pharmacology & Therapeutics. 113(5). 1036–1047. 7 indexed citations
5.
Verma, Shefali S., Binglan Li, Marjorie Risman, et al.. (2022). Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population. Journal of Translational Medicine. 20(1). 550–550. 16 indexed citations
6.
Verma, Shefali S., Anastasia Lucas, Xinyuan Zhang, et al.. (2018). Collective feature selection to identify crucial epistatic variants. BioData Mining. 11(1). 5–5. 21 indexed citations
7.
Verma, Anurag, Yuki Bradford, Scott Dudek, et al.. (2018). A simulation study investigating power estimates in phenome-wide association studies. BMC Bioinformatics. 19(1). 120–120. 60 indexed citations
8.
Hall, Molly A., John R. Wallace, Anastasia Lucas, et al.. (2017). PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies. Nature Communications. 8(1). 1167–1167. 21 indexed citations
9.
Kim, Dokyoon, Ruowang Li, Scott Dudek, & Marylyn D. Ritchie. (2015). Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. Journal of Biomedical Informatics. 56. 220–228. 24 indexed citations
10.
Hohman, Timothy J., William S. Bush, Lan Jiang, et al.. (2015). Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium. Neurobiology of Aging. 38. 141–150. 39 indexed citations
11.
12.
Bush, William S., Jacob L. McCauley, Philip L. DeJager, et al.. (2011). A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility. Genes and Immunity. 12(5). 335–340. 23 indexed citations
13.
Cattaert, Tom, M. Luz Calle, Scott Dudek, et al.. (2010). Model-Based Multifactor Dimensionality Reduction for detecting epistasis in case-control data in the presence of noise. Annals of Human Genetics. 75(1). 78–89. 67 indexed citations
14.
Pendergrass, Sarah A., Scott Dudek, Dan M. Roden, Dana C. Crawford, & Marylyn D. Ritchie. (2010). VISUAL INTEGRATION OF RESULTS FROM A LARGE DNA BIOBANK (BIOVU) USING SYNTHESIS-VIEW. WORLD SCIENTIFIC eBooks. 265–275. 9 indexed citations
15.
Pendergrass, Sarah A., Scott Dudek, Dana C. Crawford, & Marylyn D. Ritchie. (2010). Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis. BioData Mining. 3(1). 10–10. 32 indexed citations
16.
17.
Bush, William S., Todd L. Edwards, Scott Dudek, Brett A. McKinney, & Marylyn D. Ritchie. (2008). Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction. BMC Bioinformatics. 9(1). 238–238. 52 indexed citations
18.
19.
Motsinger‐Reif, Alison A., Scott Dudek, Lance W. Hahn, & Marylyn D. Ritchie. (2008). Comparison of approaches for machine‐learning optimization of neural networks for detecting gene‐gene interactions in genetic epidemiology. Genetic Epidemiology. 32(4). 325–340. 71 indexed citations
20.
Glossop, Nicholas R.J., Jerry H. Houl, Hao Zheng, et al.. (2003). VRILLE Feeds Back to Control Circadian Transcription of Clock in the Drosophila Circadian Oscillator. Neuron. 37(2). 249–261. 229 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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