Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

ABSTRACT: Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitati

SUBMITTER: Asif M 

PROVIDER: S-EPMC6287949 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature