Staff View: A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

Background: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequen...

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Main Authors:	Eissa, Noureldin S., Khairuddin, Uswah, Yusof, Rubiyah
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2022
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/101266/1/UswahKhairuddin2022_AHybridMetaheuristicDeepLearningTechnique.pdf http://eprints.utm.my/id/eprint/101266/ http://dx.doi.org/10.1186/s12859-022-04815-7
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spelling	my.utm.1012662023-06-08T08:19:57Z http://eprints.utm.my/id/eprint/101266/ A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation Eissa, Noureldin S. Khairuddin, Uswah Yusof, Rubiyah T Technology (General) Background: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results: In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions: This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data. BioMed Central Ltd 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/101266/1/UswahKhairuddin2022_AHybridMetaheuristicDeepLearningTechnique.pdf Eissa, Noureldin S. and Khairuddin, Uswah and Yusof, Rubiyah (2022) A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation. Bioinformatics, 23 (1). pp. 1-23. ISSN 1471-2105 http://dx.doi.org/10.1186/s12859-022-04815-7 DOI : 10.1186/s12859-022-04815-7
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Eissa, Noureldin S. Khairuddin, Uswah Yusof, Rubiyah A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
description	Background: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results: In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions: This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data.
format	Article
author	Eissa, Noureldin S. Khairuddin, Uswah Yusof, Rubiyah
author_facet	Eissa, Noureldin S. Khairuddin, Uswah Yusof, Rubiyah
author_sort	Eissa, Noureldin S.
title	A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
title_short	A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
title_full	A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
title_fullStr	A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
title_full_unstemmed	A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation
title_sort	hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on dna methylation
publisher	BioMed Central Ltd
publishDate	2022
url	http://eprints.utm.my/id/eprint/101266/1/UswahKhairuddin2022_AHybridMetaheuristicDeepLearningTechnique.pdf http://eprints.utm.my/id/eprint/101266/ http://dx.doi.org/10.1186/s12859-022-04815-7
_version_	1769842029503709184
score	13.211869

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

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