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Volume 9
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10.3390/pr9081466
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Review

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

by
Aina Umairah Mazlan
1,
Noor Azida Sahabudin
1,*,
Muhammad Akmal Remli
2,3,*,
Nor Syahidatul Nadiah Ismail
1,
Mohd Saberi Mohamad
4,
Hui Wen Nies
5 and
Nor Bakiah Abd Warif
6
1
Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
2
Institute for Artificial Intelligence and Big Data, City Campus, Pengkalan Chepa, Universiti Malaysia Kelantan, Kota Bharu 16100, Kelantan, Malaysia
3
Department of Data Science, City Campus, Universiti Malaysia Kelantan, Pengkalan Chepa, Kota Bharu 16100, Kelantan, Malaysia
4
Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, AI Ain P.O. Box 17666, United Arab Emirates
5
Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
6
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia
*
Authors to whom correspondence should be addressed.
Processes 2021, 9(8), 1466; https://doi.org/10.3390/pr9081466
Submission received: 6 July 2021 / Revised: 8 August 2021 / Accepted: 18 August 2021 / Published: 22 August 2021
(This article belongs to the Special Issue Advanced Technologies in Biohydrogen and Bioprocesses)
Review Reports Versions Notes

Abstract

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.
Keywords: machine learning; deep learning; cancer classification; biomarker; gene expression machine learning; deep learning; cancer classification; biomarker; gene expression

Share and Cite

MDPI and ACS Style

Mazlan, A.U.; Sahabudin, N.A.; Remli, M.A.; Ismail, N.S.N.; Mohamad, M.S.; Nies, H.W.; Abd Warif, N.B. A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes 2021, 9, 1466. https://doi.org/10.3390/pr9081466

AMA Style

Mazlan AU, Sahabudin NA, Remli MA, Ismail NSN, Mohamad MS, Nies HW, Abd Warif NB. A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes. 2021; 9(8):1466. https://doi.org/10.3390/pr9081466

Chicago/Turabian Style

Mazlan, Aina Umairah, Noor Azida Sahabudin, Muhammad Akmal Remli, Nor Syahidatul Nadiah Ismail, Mohd Saberi Mohamad, Hui Wen Nies, and Nor Bakiah Abd Warif. 2021. "A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data" Processes 9, no. 8: 1466. https://doi.org/10.3390/pr9081466

APA Style

Mazlan, A. U., Sahabudin, N. A., Remli, M. A., Ismail, N. S. N., Mohamad, M. S., Nies, H. W., & Abd Warif, N. B. (2021). A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes, 9(8), 1466. https://doi.org/10.3390/pr9081466

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