By Allison Proffitt
August 24, 2015 | Last month, Brendan Frey got a flurry of media attention as he and his cofounders formally launched Deep Genomics, a company whose mission is to combine deep learning with genomics to model the body.
By the time Frey and I got a chance to discuss the venture, his company-genesis spiel was perfected, rolling off the tongue in one 1,200-word monologue.
But Frey isn't a pitchman. Frey’s background is in machine learning, specifically computer vision and speech processing. About twelve years ago, Frey started asked questions about genomics, and was dissatisfied with the answers. As he dug into the literature, he found genome-wide association studies were common and the methodology struck Frey as particularly “ill-conceived.”
“They address questions which I think are kind of superficial,” he explained. “You try to just correlate mutations with diseases. It doesn’t get at the causation. It doesn’t get at why the mutation is a problem.”
Frey directed his lab at the University of Toronto to apply machine learning techniques to the genome: to read the “text” and predict the phenotypic outcomes. It would take a strong foundation in machine learning and an equally deep understanding of genome and cell biology: “to understand how DNS wraps around nucleosomes like chromatin. Understanding how RNA secondary structures appear. Understanding how proteins bind to RNA, how proteins bind to DNA. Understanding all of the biological components and cellular processes that underlie cell biology.”
Frey equipped his lab with both skillsets and started publishing: 47 papers since 2003 in Nature, Science, Nature Biotechnology, Nature Genetics, Bioinformatics, and more. Only two of which were GWAS studies.
But Frey soon realized it wasn’t enough to just publish the studies. “About a year ago my cofounders and I decided that if we really wanted to change medicine we needed to make a company.” Frey hired many of his University of Toronto lab group, and Deep Genomics was born.