A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.

ABSTRACT:

Background

Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements.

Results

In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. We construct datasets for three species: Human, Mouse and Drosophila. For each species, we compile the balanced dataset and imbalanced dataset, and thus obtain six datasets to build and evaluate prediction models. In the computational experiments, the genetic algorithm-based weighted ensemble method achieves 10-fold cross val

SUBMITTER: Li D 

PROVIDER: S-EPMC5006569 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature