Building end-to-end machine learning systems for providing personalized health and ancestry insights

Event Date: April 6, 2023
Time: 7:30 pm
Location: HAMP 2118
Priority: No
School or Program: College of Engineering
College Calendar: Show

Discussion with 23AndMe Director Subarna Sinha

Host: Dr. Somali Chaterji, assistant professor of agricultural and biological engineering and electrical and computer engineering (by courtesy), CEO of KeyByte

Speaker via Zoom: Dr. Subarna Sinha, director of data and machine learning, 23andMe

Hosted by Innovatory for Cells and Neural Machines (ICAN) with the IEEE EMBS Midwest Region and Purdue Chapter, and the Purdue Society of Women Engineers

Tremendous advances in genomics over the last couple of decades have made it relatively cheap to identify DNA variants at millions of locations on the genome. 23andMe has accumulated large amounts of genetic data from their 13M+ customers, >80% of whom have consented that their data be used for research purposes. Besides genetic data, they also have non-genetic data that capture diverse information related to diet, lifestyle, disease status, etc. These data offer the unique ability to build machine learning models that can provide customers personalized insights about their health and/or ancestry. In this talk, we will provide a broad overview of the types of health and ancestry insights that can be generated using the data. We will also describe an end-to-end machine learning system that can train and deploy these models at scale.

Monique McClain
Dr. Somali Chaterji
Monique McClain
Dr. Subarna Sinha