An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets

Choon, Sen Seah and Shahreen, Kasim and Mohd Farhan, Md Fudzee and Jeffrey Mark, Law Tze Ping and Mohd Saberi, Mohamad and Saedudin, Rd Rohmat and Mohd Arfian, Ismail (2017) An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets. Saudi Journal of Biological Sciences, 24 (8). pp. 1828-1841. ISSN 1319-562X. (Published)

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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 data- set 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 signifi- cant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Item Type: Article
Uncontrolled Keywords: Directed random walk algorithm; Group specific tuning parameter; Pathway
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Dr. Mohd Arfian Ismail
Date Deposited: 18 Jan 2018 05:57
Last Modified: 27 Mar 2018 03:20
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