Digital Image Processing to Detect Adaptive Evolution.

ABSTRACT: In recent years, advances in image processing and machine learning have fueled a paradigm shift in detecting genomic regions under natural selection. Early machine learning techniques employed population-genetic summary statistics as features, which focus on specific genomic patterns expected by adaptive and neutral processes. Though such engineered features are important when training data are limited, the ease at which simulated data can now be generated has led to the recent development of approaches that take in image representations of haplotype alignments and automatically extract important features using convolutional neural networks. Digital image processing methods termed α-molecules are a class of techniques for multiscale representation of objects that can extract a diverse set

SUBMITTER: Amin MR 

PROVIDER: S-EPMC11631197 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature