AccScience Publishing / IJOSI / Volume 8 / Issue 4 / DOI: 10.6977/IJoSI.202412_8(4).0004
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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

Jayakrishnan R1 ,  S. Meera2*
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1 Department of Computer Science and EngineeringVels Institute of Science, Technology and Advanced StudiesChennai, Tamil Nadu
2 Department of Computer Science and EngineeringVels Institute of Science, Technology and Advanced StudiesChennai, Tamil Nadu
IJOSI 2024 , 8(4), 46–66; https://doi.org/10.6977/IJoSI.202412_8(4).0004
Submitted: 23 February 2024 | Revised: 1 July 2024 | Accepted: 3 September 2024 | Published: 30 December 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The word "cancer" denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and under-standable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analysevariousmethods for multiclass categorising cancer subtypes usingdeep structured reinforcement learn-ing (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combin-ing the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data.

Keywords
Cancer subtypes
Machine-learning
Deep-learning
Reinforcement learning
Gene expression
Deep neural networks.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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