Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data

Cancer markers play a significant role in the diagnosis of the origin of cancers and in the detection of cancers from initial treatments. This is a challenging task owing to the heterogeneity nature of cancers. Identification of these markers could help in improving the survival rate of cancer patie...

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Main Author:	Nies, Hui Wen
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/98108/1/NiesHuiWenPSC2020.pdf
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spelling	my-utm-ep.981082022-11-14T10:07:17Z Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data 2020 Nies, Hui Wen QA75 Electronic computers. Computer science Cancer markers play a significant role in the diagnosis of the origin of cancers and in the detection of cancers from initial treatments. This is a challenging task owing to the heterogeneity nature of cancers. Identification of these markers could help in improving the survival rate of cancer patients, in which dedicated treatment can be provided according to the diagnosis or even prevention. Previous investigations show that the use of pathway topology information could help in the detection of cancer markers from gene expression. Such analysis reduces its complexity from thousands of genes to a few hundreds of pathways. However, most of the existing methods group different cancer subtypes into just disease samples, and consider all pathways contribute equally in the analysis process. Meanwhile, the interaction between multiple genes and the genes with missing edges has been ignored in several other methods, and hence could lead to the poor performance of the identification of cancer markers from gene expression. Thus, this research proposes enhanced directed random walk to identify pathway and gene markers for multiclass cancer gene expression data. Firstly, an improved pathway selection with analysis of variances (ANOVA) that enables the consideration of multiple cancer subtypes is performed, and subsequently the integration of k-mean clustering and average silhouette method in the directed random walk that considers the interaction of multiple genes is also conducted. The proposed methods are tested on benchmark gene expression datasets (breast, lung, and skin cancers) and biological pathways. The performance of the proposed methods is then measured and compared in terms of classification accuracy and area under the receiver operating characteristics curve (AUC). The results indicate that the proposed methods are able to identify a list of pathway and gene markers from the datasets with better classification accuracy and AUC. The proposed methods have improved the classification performance in the range of between 1% and 35% compared with existing methods. Cell cycle and p53 signaling pathway were found significantly associated with breast, lung, and skin cancers, while the cell cycle was highly enriched with squamous cell carcinoma and adenocarcinoma. 2020 Thesis http://eprints.utm.my/id/eprint/98108/ http://eprints.utm.my/id/eprint/98108/1/NiesHuiWenPSC2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143755 phd doctoral Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nies, Hui Wen Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
description	Cancer markers play a significant role in the diagnosis of the origin of cancers and in the detection of cancers from initial treatments. This is a challenging task owing to the heterogeneity nature of cancers. Identification of these markers could help in improving the survival rate of cancer patients, in which dedicated treatment can be provided according to the diagnosis or even prevention. Previous investigations show that the use of pathway topology information could help in the detection of cancer markers from gene expression. Such analysis reduces its complexity from thousands of genes to a few hundreds of pathways. However, most of the existing methods group different cancer subtypes into just disease samples, and consider all pathways contribute equally in the analysis process. Meanwhile, the interaction between multiple genes and the genes with missing edges has been ignored in several other methods, and hence could lead to the poor performance of the identification of cancer markers from gene expression. Thus, this research proposes enhanced directed random walk to identify pathway and gene markers for multiclass cancer gene expression data. Firstly, an improved pathway selection with analysis of variances (ANOVA) that enables the consideration of multiple cancer subtypes is performed, and subsequently the integration of k-mean clustering and average silhouette method in the directed random walk that considers the interaction of multiple genes is also conducted. The proposed methods are tested on benchmark gene expression datasets (breast, lung, and skin cancers) and biological pathways. The performance of the proposed methods is then measured and compared in terms of classification accuracy and area under the receiver operating characteristics curve (AUC). The results indicate that the proposed methods are able to identify a list of pathway and gene markers from the datasets with better classification accuracy and AUC. The proposed methods have improved the classification performance in the range of between 1% and 35% compared with existing methods. Cell cycle and p53 signaling pathway were found significantly associated with breast, lung, and skin cancers, while the cell cycle was highly enriched with squamous cell carcinoma and adenocarcinoma.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Nies, Hui Wen
author_facet	Nies, Hui Wen
author_sort	Nies, Hui Wen
title	Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
title_short	Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
title_full	Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
title_fullStr	Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
title_full_unstemmed	Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
title_sort	identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2020
url	http://eprints.utm.my/id/eprint/98108/1/NiesHuiWenPSC2020.pdf
_version_	1776100551372570624

Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data

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