Genome-wide prediction of dominant and recessive neurodevelopmental disorder-associated genes.

ABSTRACT: Despite great progress, thousands of neurodevelopmental disorder (NDD) risk genes remain to be discovered. We present a computational approach that accelerates NDD risk gene identification using machine learning. First, we demonstrate that models trained solely on single-cell RNA sequencing data can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Notably, we find differences in gene expression patterns of genes with monoallelic and bi-allelic inheritance patterns in the developing human cortex. We then integrate expression data with 300 orthogonal features, including intolerance metrics, protein-protein interaction data, and others, in a semi-supervised machine learning framework (mantis-ml

SUBMITTER: Dhindsa RS 

PROVIDER: S-EPMC11947176 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature