Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

ABSTRACT: MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection

SUBMITTER: Yousef M 

PROVIDER: S-EPMC4844869 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature