POL Scientific / JBM / Volume 11 / Issue 3 / DOI: 10.14440/jbm.2024.0016
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Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization

Massoud Sokouti1,2,3 ,  Babak Sokouti4*
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1 Research Center of Evidence-Based Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
2 Health Promotion Research Center, Tabriz Medical Sciences, Islamic Azad University, Tabriz, Iran
3 Department of Physiology, Faculty of Medicine, Tabriz Medical Sciences, Islamic Azad University, Tabriz, Iran
4 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
JBM 2024 , 11(3), e99010017; https://doi.org/10.14440/jbm.2024.0016
Submitted: 17 June 2024 | Revised: 10 July 2024 | Accepted: 22 July 2024 | Published: 9 August 2024
© 2024 by the Journal of Biological Methods published by POL Scientific. Licensee POL Scientific, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Gene expression data are used to discover meaningful hidden information in gene datasets. Cancer and other disorders may be diagnosed based on differences in gene expression profiles, and this information can be gleaned by gene sequencing. Thanks to the tremendous power of artificial intelligence (AI), healthcare has become a significant user of deep learning (DL) for predicting cancer diseases and categorizing gene expression. Gene expression Microarrays have been proved effective in predicting cancer diseases and categorizing gene expression. Gene expression datasets contain only limited samples, but the features of cancer are diverse and complex. To overcome their dimensionality, gene expression datasets must be enhanced. By learning and analyzing features of input data, it is possible to extract features, as multidimensional arrays, from the data. Synthetic samples are needed to strengthen the range of information. DL strategies may be used when gene expression data are used to diagnose and classify cancer diseases.

Keywords
Gene expression
High dimensionality
Deep learning
Cancer
Funding
None.
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The authors declare that they have no conflicts of interest.
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