TY - JOUR
T1 - Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
AU - Tran, Thi-Oanh
AU - Vo, Thanh Hoa
AU - Le, Nguyen Quoc Khanh
N1 - © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2023/7/29
Y1 - 2023/7/29
N2 - Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.
AB - Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.
KW - decision making
KW - deep learning
KW - genomics
KW - lung cancer
KW - omics data
KW - therapeutic development
U2 - 10.1093/bfgp/elad031
DO - 10.1093/bfgp/elad031
M3 - Article
C2 - 37519050
SN - 2041-2649
JO - Briefings in Functional Genomics
JF - Briefings in Functional Genomics
M1 - elad031
ER -