On the evaluation of the fidelity of supervised classifiers in the prediction of chimeric RNAs.

ABSTRACT:

Background

High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis.

Results

The task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning proble

SUBMITTER: Beaumeunier S 

PROVIDER: S-EPMC5090896 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature