Affiliations 

  • 1 Soft Computing and Data Mining Centre, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn, Malaysia
  • 2 Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Karung Berkunci 01, 16300 Bachok, Kelantan, Malaysia
  • 3 School of Industrial Engineering, Telkom University, 40257 Bandung, West Java, Indonesia
  • 4 Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia
Saudi J Biol Sci, 2017 Dec;24(8):1828-1841.
PMID: 29551932 DOI: 10.1016/j.sjbs.2017.11.024

Abstract

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.