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组学和人工智能在癌症中的研究与应用。

Research and application of omics and artificial intelligence in cancer.

机构信息

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.

出版信息

Phys Med Biol. 2024 Oct 18;69(21). doi: 10.1088/1361-6560/ad6951.

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.

摘要

癌症具有较高的发病率和致死率,严重威胁人类健康。随着高通量技术的发展,不同类型的癌症基因组学数据不断积累,包括基因组学、表观基因组学、转录组学、蛋白质组学和代谢组学。为了了解肿瘤发生的潜在机制,需要对各种组学数据进行综合分析。然而,整合如此大量的数据是当前面临的主要挑战之一。人工智能(AI)技术,如机器学习,现在已成为分析和理解疾病多组学数据的实用工具。这为癌症筛查、诊断和治疗的现有研究范式提供了极大的优化。此外,随着医疗信息化的发展,智能医疗也受到了广泛关注。作为创新医疗的重要组成部分,对癌症患者进行实用、智能的预后分析和个性化治疗也是必要的。本文介绍了近年来先进的多组学数据分析技术,展示了组学数据与 AI 结合应用于癌症疾病的案例和优势,最后简要描述了多组学分析和 AI 当前阶段面临的挑战,旨在为肿瘤学研究提供新的视角和实现个性化癌症治疗的可能性。

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