Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci.

ABSTRACT: Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species

SUBMITTER: Georgakilas GK 

PROVIDER: S-EPMC7289789 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature