GTC Silicon Valley-2019: Understanding Genome Regulation with Interpretable Deep Learning
GTC Silicon Valley-2019 ID:S9632:Understanding Genome Regulation with Interpretable Deep Learning
Avanti Shrikumar(Stanford University)
We'll discuss how interpretable deep learning can significantly advance our understanding of genomic regulation. All our cells have the same DNA sequence, yet different cell-types express different genes in a process called genomic regulation. This regulation is driven by binding regulatory proteins to DNA. The vast majority of disease-associated mutations do not disrupt the DNA sequences of genes, but rather disrupt DNA sequences important for regulatory protein binding. Unfortunately, conventional computational models fail to explain which regulatory proteins are impacted for over 90 percent of such mutations. We show that by using deep learning coupled with our interpretation algorithms DeepLIFT (https://github.com/kundajelab/deeplift) and TF-MoDISco (https://github.com/kundajelab/tfmodisco) we can explain a substantially greater fraction of mutations that impact genomic regulation and obtain novel biological insights that are not provided by other methods.