A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.

ABSTRACT:

Background

MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria.

Results

This study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster member

SUBMITTER: Yang Y 

PROVIDER: S-EPMC5374636 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature