He Xiujing, Liu Xiaowei, Zuo Fengli, Shi Hubing, Jing Jing
Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China.
Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China.
Semin Cancer Biol. 2023 Jan;88:187-200. doi: 10.1016/j.semcancer.2022.12.009. Epub 2022 Dec 31.
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.
随着生物技术的进步，创新的组学技术不断涌现，使研究人员能够从基因组、表观基因组、转录组、蛋白质组、代谢组等获取多层次信息。大量的组学技术，包括批量和单细胞组学方法，已使人们能够以前所未有的规模和分辨率对不同分子层进行表征，从而提供肿瘤行为的整体视图。多组学分析允许在每个生物层系统地探究各种分子信息，但在如何从呈指数增长的多组学数据中提取有价值的见解方面带来了棘手的挑战。因此，需要高效的算法来降低数据维度，同时剖析癌症复杂生物学过程背后的奥秘。人工智能已展示出分析肿瘤学领域内互补多模态数据流的能力。多组学技术与人工智能算法的同步发展推动了癌症精准医学的发展。在此，我们介绍了最先进的组学技术，并概述了使用人工智能策略进行多组学整合分析的路线图。描述了基于人工智能的多组学方法所取得的进展，特别是在早期癌症筛查、诊断、反应评估和预后预测方面。最后，我们讨论了多组学分析面临的挑战以及该领域未来的初步趋势。随着人工智能在多组学分析中的应用不断增加，我们预计基于人工智能的多组学技术将推动精准医学范式的转变。
