An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ig...

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Main Authors:	Xin Hui Tay, Xin Hui Tay, Kasim, Shahreen, Tole Sutikno, Tole Sutikno, Md Fudzee, Mohd Farhan, Hassan, Rohayanti, Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir, Aziz, Norshakirah, Choon Sen Seah, Choon Sen Seah
Format:	Article
Language:	English
Published:	Mdpi 2023
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/10072/1/J16097_fc692a6f023a80413e40b199966c0376.pdf

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author	Xin Hui Tay, Xin Hui Tay Kasim, Shahreen Tole Sutikno, Tole Sutikno Md Fudzee, Mohd Farhan Hassan, Rohayanti Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir Aziz, Norshakirah Choon Sen Seah, Choon Sen Seah
author_facet	Xin Hui Tay, Xin Hui Tay Kasim, Shahreen Tole Sutikno, Tole Sutikno Md Fudzee, Mohd Farhan Hassan, Rohayanti Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir Aziz, Norshakirah Choon Sen Seah, Choon Sen Seah
author_sort	Xin Hui Tay, Xin Hui Tay
collection	UTHM
description	The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The withindataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types.
first_indexed	2024-03-05T22:04:23Z
format	Article
id	uthm.eprints-10072
institution	Universiti Tun Hussein Onn Malaysia
language	English
last_indexed	2024-03-05T22:04:23Z
publishDate	2023
publisher	Mdpi
record_format	dspace
spelling	uthm.eprints-100722023-10-11T03:23:42Z http://eprints.uthm.edu.my/10072/ An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks Xin Hui Tay, Xin Hui Tay Kasim, Shahreen Tole Sutikno, Tole Sutikno Md Fudzee, Mohd Farhan Hassan, Rohayanti Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir Aziz, Norshakirah Choon Sen Seah, Choon Sen Seah T Technology (General) The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The withindataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10072/1/J16097_fc692a6f023a80413e40b199966c0376.pdf Xin Hui Tay, Xin Hui Tay and Kasim, Shahreen and Tole Sutikno, Tole Sutikno and Md Fudzee, Mohd Farhan and Hassan, Rohayanti and Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir and Aziz, Norshakirah and Choon Sen Seah, Choon Sen Seah (2023) An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks. Genes, 14 (574). pp. 1-13. https://doi.org/10.3390/genes14030574
spellingShingle	T Technology (General) Xin Hui Tay, Xin Hui Tay Kasim, Shahreen Tole Sutikno, Tole Sutikno Md Fudzee, Mohd Farhan Hassan, Rohayanti Emelia Akashah Patah Akhir, Emelia Akashah Patah Akhir Aziz, Norshakirah Choon Sen Seah, Choon Sen Seah An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title	An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title_full	An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title_fullStr	An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title_full_unstemmed	An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title_short	An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
title_sort	entropy based directed random walk for cancer classification using gene expression data based on bi random walk on two separated networks
topic	T Technology (General)
url	http://eprints.uthm.edu.my/10072/1/J16097_fc692a6f023a80413e40b199966c0376.pdf
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An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks

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