BERMP: a cross-species classifier for predicting m⁶A sites by integrating a deep learning algorithm and a random forest approach.

ABSTRACT: N⁶-methyladenosine (m⁶A) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of m⁶A sites identified from different species using high-throughput experiments provides a rich resource to construct in-silico approaches for identifying m⁶A sites. The existing m⁶A predictors are developed using conventional machine-learning (ML) algorithms and most are species-centric. In this paper, we develop a novel cross-species deep-learning classifier based on bidirectional Gated Recurrent Unit (BGRU) for the prediction of m⁶A sites. In comparison with conventional ML approaches, BGRU achieves outstanding performance for the Mammalia dataset that contains over fifty thou

SUBMITTER: Huang Y 

PROVIDER: S-EPMC6216033 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature