Data classification and weighted evidence accumulation to detect relevant pathology

Cancer is considered as one of the world's most serious illnesses. There are more than 100 types of cancer, which can bring major national burden for countries. MicroRNAs (miRNAs) are a class of small noncoding ribonucleic acids (RNAs) that have a crucial part of cancer tissue formation and some miRNAs are differentially expressed in a normal and cancerous tumor. Therefore, it is possible to diagnose cancer by analysis of individual's miRNAs, which it is not an easy process, because of the huge number of miRNAs. In this regard, informative miRNAs selection can play an important role to diagnose cancer. The interest of this paper is to improve the performance of miRNAs selection by using different classification methods on representative miRNAs of normal and cancer class, which is determined based on FMIMS and combine its results by our proposed approach named Weighted Evidence Accumulation (W-EAC). The performances of this method are evaluated on Gene Expression Omnibus (GEO repository) consisting of the samples from Pancreas Cancer, Nasopharyngeal Cancer, Colorectal Cancer, Lung Cancer and Melanoma Cancer.