Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.

ABSTRACT:

Background

MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results.

Results

An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction ta

SUBMITTER: Lopez-Rincon A 

PROVIDER: S-EPMC6751684 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature