Learning maximally spanning representations improves protein function annotation.

ABSTRACT: Automated protein function annotation is a fundamental problem in computational biology, crucial for understanding the functional roles of proteins in biological processes, with broad implications in medicine and biotechnology. A persistent challenge in this problem is the imbalanced, long-tail distribution of available function annotations: a small set of well-studied function classes account for most annotated proteins, while many other classes have few annotated proteins, often due to investigative bias, experimental limitations, or intrinsic biases in protein evolution. As a result, existing machine learning models for protein function prediction tend to only optimize the prediction accuracy for well-studied function classes overrepresented in the training data, leading to poor accurac

SUBMITTER: Luo J 

PROVIDER: S-EPMC11870436 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature