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How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases.

, , , , and . Artif. Life, 26 (1): 23-37 (2020)

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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)Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity., , , and . BioData Min., (2015)The multiscale backbone of the human phenotype network based on biological pathways., , , , , and . BioData Min., (2014)The role of visualization and 3-D printing in biological data mining., , , , , , and . BioData Min., (2015)Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions., , , and . BioData Min., (2009)Mining the diseasome., and . BioData Min., (2011)Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery., , , and . BioData Min., 10 (1): 13:1-13:18 (2017)Session introduction., , , and . Pacific Symposium on Biocomputing, page 200-202. World Scientific Publishing, (2012)Enabling Personal Genomics with an Explicit Test of Epistasis., , , , , , , and . Pacific Symposium on Biocomputing, page 327-336. World Scientific Publishing, (2010)