Year: 2017
Type: article
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.
Authors: Shinuk Kim, Hyowon Lee, Mark Kon
Institutions: Sangmyung University, Boston University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Award: G185749233
OpenAlex ID: W4240273566