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A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model...

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Main Authors: Mazlan, A. U., Sahabudin, N. A., Remli, M. A., Ismail, N. S. N., Mohamad, M. S., Nies, H. W., Warif, N. B. A.
Format: Article
Language:English
Published: MDPI AG 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/94998/1/AinaUmairahMazlan2021_AReviewonRecentProgress.pdf
http://eprints.utm.my/id/eprint/94998/
http://dx.doi.org/10.3390/pr9081466
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id my.utm.94998
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spelling my.utm.949982022-04-29T22:01:11Z http://eprints.utm.my/id/eprint/94998/ A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data Mazlan, A. U. Sahabudin, N. A. Remli, M. A. Ismail, N. S. N. Mohamad, M. S. Nies, H. W. Warif, N. B. A. QA75 Electronic computers. Computer science Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications. MDPI AG 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94998/1/AinaUmairahMazlan2021_AReviewonRecentProgress.pdf Mazlan, A. U. and Sahabudin, N. A. and Remli, M. A. and Ismail, N. S. N. and Mohamad, M. S. and Nies, H. W. and Warif, N. B. A. (2021) A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data. Processes, 9 (8). ISSN 2227-9717 http://dx.doi.org/10.3390/pr9081466 DOI: 10.3390/pr9081466
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 QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mazlan, A. U.
Sahabudin, N. A.
Remli, M. A.
Ismail, N. S. N.
Mohamad, M. S.
Nies, H. W.
Warif, N. B. A.
A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
description Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.
format Article
author Mazlan, A. U.
Sahabudin, N. A.
Remli, M. A.
Ismail, N. S. N.
Mohamad, M. S.
Nies, H. W.
Warif, N. B. A.
author_facet Mazlan, A. U.
Sahabudin, N. A.
Remli, M. A.
Ismail, N. S. N.
Mohamad, M. S.
Nies, H. W.
Warif, N. B. A.
author_sort Mazlan, A. U.
title A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
title_short A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
title_full A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
title_fullStr A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
title_full_unstemmed A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
title_sort review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
publisher MDPI AG
publishDate 2021
url http://eprints.utm.my/id/eprint/94998/1/AinaUmairahMazlan2021_AReviewonRecentProgress.pdf
http://eprints.utm.my/id/eprint/94998/
http://dx.doi.org/10.3390/pr9081466
_version_ 1732945419657281536
score 13.211869

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