Abstract
Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.
Highlights
To improve prognosis of cancer patients, there is a growing trend to analyze numerous types of omics data, such as DNA, RNA, microRNA, protein, and metabolites [1, 2]
The recent advanced omics-technologies allow us to conduct single cell multi-omics sequencing, which can characterize the unique genotype and phenotype of each individual cell. This approach can provide new insights into tumor heterogeneity and deep characterization of the tumor microenvironment at a single-cell resolution [13]. Integration of these diverse omics data with highly accurate clinical information should lead to new clinical developments regarding the prevention of cancer onset and new treatment strategies based on intratumoral heterogeneity
In 2011, the National Lung Screening Trial (NLST) showed that low-dose computed tomography (CT) (LD-CT) screening for lung cancer reduced the relative mortality by 20% [42]
Summary
To improve prognosis of cancer patients, there is a growing trend to analyze numerous types of omics data, such as DNA, RNA, microRNA, protein, and metabolites [1, 2]. The recent advanced omics-technologies allow us to conduct single cell multi-omics sequencing, which can characterize the unique genotype and phenotype of each individual cell. This approach can provide new insights into tumor heterogeneity and deep characterization of the tumor microenvironment at a single-cell resolution [13]. Integration of these diverse omics data with highly accurate clinical information should lead to new clinical developments regarding the prevention of cancer onset and new treatment strategies based on intratumoral heterogeneity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
