Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Biology 2023, 12(7), 893; https://doi.org/10.3390/biology12070893
Submission received: 17 May 2023
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Revised: 16 June 2023
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Accepted: 20 June 2023
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Published: 21 June 2023
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
Simple Summary
The ongoing advancements in deep learning, notably its use in predicting cancer survival through genomic data analysis, calls for an up-to-date review. This paper inspects notable works from 2021 to 2023, underlining essential developments and their implications in the field. We offer a comprehensive review of the research, selective paper choice, and thorough analysis of prevailing trends, contributing to a better understanding of deep learning’s potential in this vital domain.
Abstract
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.
