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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

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, Shahreen Kasim, Shahreen Kasim, Tole Sutikno, Tole Sutikno, Md Fudzee, Mohd Farhan, Hassan, Rohayanti, Patah Akhir, Emelia Akashah, 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/9586/1/J16097_fc692a6f023a80413e40b199966c0376.pdf
http://eprints.uthm.edu.my/9586/
https://doi.org/10.3390/genes14030574
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spelling my.uthm.eprints.95862023-08-07T02:25:48Z http://eprints.uthm.edu.my/9586/ 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 Shahreen Kasim, Shahreen Kasim Tole Sutikno, Tole Sutikno Md Fudzee, Mohd Farhan Hassan, Rohayanti Patah Akhir, Emelia Akashah 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/9586/1/J16097_fc692a6f023a80413e40b199966c0376.pdf Xin Hui Tay, Xin Hui Tay and Shahreen Kasim, Shahreen Kasim and Tole Sutikno, Tole Sutikno and Md Fudzee, Mohd Farhan and Hassan, Rohayanti and Patah Akhir, Emelia Akashah 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
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Xin Hui Tay, Xin Hui Tay
Shahreen Kasim, Shahreen Kasim
Tole Sutikno, Tole Sutikno
Md Fudzee, Mohd Farhan
Hassan, Rohayanti
Patah Akhir, Emelia Akashah
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
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.
format Article
author Xin Hui Tay, Xin Hui Tay
Shahreen Kasim, Shahreen Kasim
Tole Sutikno, Tole Sutikno
Md Fudzee, Mohd Farhan
Hassan, Rohayanti
Patah Akhir, Emelia Akashah
Aziz, Norshakirah
Choon Sen Seah, Choon Sen Seah
author_facet Xin Hui Tay, Xin Hui Tay
Shahreen Kasim, Shahreen Kasim
Tole Sutikno, Tole Sutikno
Md Fudzee, Mohd Farhan
Hassan, Rohayanti
Patah Akhir, Emelia Akashah
Aziz, Norshakirah
Choon Sen Seah, Choon Sen Seah
author_sort Xin Hui Tay, Xin Hui Tay
title 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_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_sort entropy-based directed random walk for cancer classification using gene expression data based on bi-random walk on two separated networks
publisher Mdpi
publishDate 2023
url http://eprints.uthm.edu.my/9586/1/J16097_fc692a6f023a80413e40b199966c0376.pdf
http://eprints.uthm.edu.my/9586/
https://doi.org/10.3390/genes14030574
_version_ 1773545920010387456
score 13.211869

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