Imbalance learning for the prediction of N⁶-Methylation sites in mRNAs.

ABSTRACT:

Background

N⁶-methyladenosine (m⁶A) is an important epigenetic modification which plays various roles in mRNA metabolism and embryogenesis directly related to human diseases. To identify m⁶A in a large scale, machine learning methods have been developed to make predictions on m⁶A sites. However, there are two main drawbacks of these methods. The first is the inadequate learning of the imbalanced m⁶A samples which are much less than the non-m⁶A samples, by their balanced learning approaches. Second, the features used by these methods are not outstanding to represent m⁶A sequence characteristics.

Results

We propose to use cost-sensitive learning ideas to resolve the imbalance data issues in the human

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC6090857 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature