Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder.

ABSTRACT: Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis rev

SUBMITTER: Garbulowski M 

PROVIDER: S-EPMC7946989 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature