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

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 rando...

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Main Authors:	Choon, Sen Seah, Shahreen, Kasim, Mohd Farhan, Md Fudzee, Jeffrey Mark, Law Tze Ping, Mohd Saberi, Mohamad, Saedudin, Rd Rohmat, Mohd Arfian, Ismail
Format:	Article
Language:	English
Published:	ScienceDirect 2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf

id	ump-19999
recordtype	eprints
spelling	ump-199992018-03-27T03:20:43Z http://umpir.ump.edu.my/id/eprint/19999/ An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail QA75 Electronic computers. Computer science 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. ScienceDirect 2017-11-20 Article PeerReviewed application/pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf 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 https://doi.org/10.1016/j.sjbs.2017.11.024 doi: 10.1016/j.sjbs.2017.11.024
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
description	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.
format	Article
author	Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail
author_facet	Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail
author_sort	Choon, Sen Seah
title	An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
title_short	An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
title_full	An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
title_fullStr	An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
title_full_unstemmed	An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
title_sort	enhanced topologically significant directed random walk in cancer classification using gene expression datasets
publisher	ScienceDirect
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf
first_indexed	2023-09-18T22:28:40Z
last_indexed	2023-09-18T22:28:40Z
_version_	1777416137918644224

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

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