Article: Comparisons of cancer classifiers based on RNA_seq and miRNA_seq Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2017 Vol.17 No.4 pp.359 - 368 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. Inderscience Publishers - linking academia, business and industry through research

Title: Comparisons of cancer classifiers based on RNA_seq and miRNA_seq

Authors: Shinuk Kim; Hyowon Lee; Mark Kon

Addresses: Department of Civil Engineering, Sangmyung University, Cheonan 31066, South Korea ' Department of Biomedical Technology, Sangmyung University, Cheonan 31066, South Korea ' Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA

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.

Keywords: feature selection; machine learning methods; classification methods; RNA_sequence; miRNA_sequence; breast invasive carcinoma; bladder urothelial carcinoma.

DOI: 10.1504/IJDMB.2017.085715

International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.4, pp.359 - 368

Received: 19 Apr 2017
Accepted: 03 May 2017
Published online: 08 Aug 2017 *

Title: Comparisons of cancer classifiers based on RNA_seq and miRNA_seq

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