AI Article Synopsis
- - Cancer poses a major threat to human health due to its high incidence and mortality, necessitating a deeper understanding of its mechanisms through comprehensive analysis of various types of genomics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics.
- - The integration of vast amounts of multi-omics data presents challenges, but artificial intelligence (AI) techniques, particularly machine learning, are emerging as effective tools for facilitating this analysis and optimizing cancer screening, diagnosis, and treatment.
- - This paper discusses recent advancements in multi-omics data analysis, highlights successful cases where AI has been applied to cancer research, and addresses the ongoing challenges in this field, ultimately aiming to enhance personalized treatment options for cancer patients.
Article Abstract
Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.
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| http://dx.doi.org/10.1088/1361-6560/ad6951 | DOI Listing |
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