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Biodata Mining|July 25, 2014
A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detectionRyan J Urbanowicz, Ambrose Ls Granizo-Mackenzie, Jeff Kiralis, et al.BMC Bioinformatics|October 1, 2020
Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analysesElisabetta Manduchi, Weixuan Fu, Joseph D Romano, et al.Bioinformatics (Oxford, England)|October 5, 2023
Aliro: an automated machine learning tool leveraging large language modelsHyunjun Choi, Jay Moran, Nicholas Matsumoto, et al.Plos One|June 9, 2009
Failure to replicate a genetic association may provide important clues about genetic architectureCasey S Greene, Nadia M Penrod, Scott M Williams, et al.Journal of the American Medical Informatics Association : JAMIA|February 28, 2013
Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approachRyan John Urbanowicz, Angeline S Andrew, Margaret Rita Karagas, et al.Genetic Epidemiology|August 4, 2022
Genetic heterogeneity: Challenges, impacts, and methods through an associative lensAlexa A Woodward, Ryan J Urbanowicz, Adam C Naj, et al.Genetic Epidemiology|July 12, 2002
Symbolic discriminant analysis of microarray data in autoimmune diseaseJason H Moore, Joel S Parker, Nancy J Olsen, et al.Journal of the American Board of Family Medicine : JABFM|November 10, 2019
Prevalence and Characterization of Yoga Mentions in the Electronic Health RecordNadia M Penrod, Selah Lynch, Sunil Thomas, et al.Artificial Life|February 7, 2020
How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human DiseasesJason H Moore, Randal S Olson, Peter Schmitt, et al.Biodata Mining|April 29, 2017
Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discoveryNathaniel M Crabtree, Jason H Moore, John F Bowyer, et al.Pageof 56