February 27, 2019

Senior data science fellow Su-In Lee and collaborator Matt Kaeberlein have received a grant of just over $3 million from the National Institutes of Health for their project “Interpretable machine learning to identify Alzheimer’s disease therapeutic targets.” In the United States alone, someone receives an Alzheimer’s diagnosis every 66 seconds, and the disease has become the sixth leading cause of death in this country.

Alzheimer’s disease (AD) currently has no cure, no prevention, and no treatment to reverse or halt its deadly progression. The recent, rapid growth of gene expression data from human brain tissues hold great promise for identifying therapeutic targets, but extremely low success rates to identify true positive biomarkers indicate fundamental problems with the current computational approach being used.

The team seeks to revolutionize the way we identify drug targets by developing novel machine learning techniques that extract meaningful and interpretable signals from noisy, big data, combined with biological validation in an animal model of AD.