Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

ABSTRACT:

Background

Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear.

Results

In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lnc

SUBMITTER: Wang J 

PROVIDER: S-EPMC7648398 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature