Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.

ABSTRACT: Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse kn

SUBMITTER: Ratajczak F 

PROVIDER: S-EPMC10632370 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature