Out of distribution learning in bioinformatics: advancements and challenges

Abstract In the dynamic and complex field of bioinformatics, the development of machine learning models capable of accurately predicting and interpreting genomic data underpins many critical applications, from disease diagnosis to drug discovery. Traditional machine learning models, however, often fail when facing with out-of-distribution (OOD) samples that deviate from their training data, leading to significant performance degradation. This review paper delves into the realm of OOD learning within bioinformatics, highlighting its crucial role in enhancing model generalization and reliability across varied genomic datasets. We provide a comprehensive overview of recent advancements in OOD learning applications, detection techniques, and the integration of foundation models. The discussion extends to various bioinformatics sub-disciplines, including drug discovery, single cell genomics, and polygenic risk score analysis, underscoring how OOD learning has facilitated notable breakthroughs in these areas. Through detailed examination of different model architectures and methods designed to address distribution shifts, we explore the potential of OOD learning to overcome the inherent limitations of standard machine learning models in bioinformatics. This review paper can be served as a valuable resource for bioinformatics researchers, offering a detailed exploration of OOD learning’s transformative impact on understanding complex genomic data and its implications for human health.