Comparisons of cancer classifiers based on RNA-seq and miRNA-seq

Abstract

Studies in computational cancer genomics have been faced with the challenge of increasing prediction accuracy of molecular datasets. Here we outline how a feature selection method combined with machine learning may help overcome this challenge for BRCA microRNA-Seq datasets, BRCA RNA-Seq and mRNA microarray datasets, and BLCA microRNA-seq and RNA-seq datasets. We used three different computational approaches: (a) support vector machine, (b) decision tree and (c) k nearest neighbours, and two different feature selection methods: (a) Fisher feature criterion and (b) infinite feature selection. Our computational approaches performed consistently better with RNA-seq datasets rather than with miRNA-seq or RNA-array datasets.