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Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors.

, , , and . GECCO Companion, page 599-602. ACM, (2023)

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Research and applications: An information-gain approach to detecting three-way epistatic interactions in genetic association studies., , , , , , , and . JAMIA, 20 (4): 630-636 (2013)Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure., , , , , , , , and . PSB, page 460-471. (2018)A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods., , , and . PSB, page 259-267. (2018)Enabling Personal Genomics with an Explicit Test of Epistasis., , , , , , , and . Pacific Symposium on Biocomputing, page 327-336. World Scientific Publishing, (2010)Session introduction., , , and . Pacific Symposium on Biocomputing, page 200-202. World Scientific Publishing, (2012)An Integrated Network Approach to Identifying Biological Pathways and Environmental Exposure Interactions in Complex Diseases., , and . PSB, page 9-20. (2016)Spectral gene set enrichment (SGSE)., , and . BMC Bioinform., (2015)Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks., , , , , and . BMC Bioinform., (2011)The multiscale backbone of the human phenotype network based on biological pathways., , , , , and . BioData Min., (2014)Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions., , , and . BioData Min., (2009)