Semi-supervised detection of natural selection with positive-unlabeled learning.

ABSTRACT: Identifying genomic regions shaped by natural selection is a central goal in evolutionary genomics. Existing machine learning methods for this task are typically trained using simulated genomic data labeled according to specific evolutionary scenarios. While effective in controlled settings, these models are limited by their reliance on explicit class labels. They can only detect the specific processes they were trained to recognize, making it difficult to interpret predictions for regions influenced by other evolutionary forces. This limitation is especially problematic when analyzing empirical genomes shaped by a mixture of adaptive, demographic, and ecological factors. One-vs.-rest strategies offer a potential alternative, but suffer from the inherent complexity of modeling all other ev

SUBMITTER: Arnab SP 

PROVIDER: S-EPMC12393379 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature