TRANSFER LEARNING FOR ROBUST PREDICTIONS IN COMPUTATIONAL GENOMICS

Abstract

The use of machine learning on large and complex biological datasets has been instrumental in deriving meaningful insights from biological experiments. Even with constant advances in high-throughput experimental strategies, biological experiments are frequently constrained by sample availability (e.g., human tumor tissue), high experimental cost, and intrinsic biological and experimental variability (e.g., sequencing errors). Biological datasets therefore frequently present unique challenges to machine learning due to differences in sample type and experimental setup, small sample sizes from complex experiments, and distribution shifts (e.g., in vitro to in vivo). This thesis focuses on leveraging transfer learning techniques that aim to improve the performance of data-limited target tasks by utilizing data-rich source tasks. Three case studies are conducted to present the challenges and motivations and introduce novel methods in computational genomics demonstrating the utility of transfer learning.