Machine learning reveals correlations of gene expression in RNA-Seq data

Shirley Pepke – The complexity of cancer has famously eluded conquering by modern medicine. Every tumor has many aberrations that drive its growth. As a result, treatments that target single vulnerabilities are typically of short-lived efficacy. After being diagnosed with advanced stage ovarian cancer in 2013, I wagered that what was needed was an algorithm capable of digesting and analyzing the complexity to provide a detailed view into the multitude of factors at work in a given tumor. To pursue this goal, I began a collaboration with Greg Ver Steeg, who specializes in analyzing big data, to bring state-of-the-art machine learning to bear on the recently released large-scale data from the Cancer Genome Atlas (TCGA).