Toward building a general purpose: omics machine learning framework for public health

Funding period: 2023–2025
Lead: Julie (Chih-yu) Chen
Total GRDI funding: $439,400

With the exponential increase in computational power and large-scale data sets, artificial intelligence (AI) and its subdiscipline machine learning are increasingly being applied to health domains.

This project aims to increase data science literacy related to omics data and facilitate the adoption of machine learning by end users for omics projects in public health and other domains. This project will also involve developing and evaluating machine learning models for tuberculosis genotyping and outbreak classification for genomic surveillance. Although these topics are diverse and use different machine learning algorithms, the team will build a generalized and standardized omics machine learning framework—genOmicsML—for a unified approach to different topics.