基于多组学的人工智能用于癌症研究。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-09 DOI:10.1016/bs.acr.2024.06.005
Lusheng Li, Mengtao Sun, Jieqiong Wang, Shibiao Wan
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引用次数: 0

摘要

随着新一代测序技术的长足进步,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在内的大量多组学数据已经积累起来,为探索癌症在不同分子水平和尺度上的异质性和复杂性提供了前所未有的机会。多组学的前景之一在于,它能够提供一个支撑癌症的生物网络和通路的整体视图,有助于深入了解癌症的发展、进展和对治疗的反应。然而,多组学研究产生的数据呈指数级增长,给分析工作带来了巨大挑战。处理、分析、整合和解释这些多组学数据集以提取有意义的见解是一项雄心勃勃的任务,也是当前癌症研究的最前沿。人工智能(AI)的应用已成为应对这些挑战的强大解决方案,在破译复杂模式和从大规模、错综复杂的 omics 数据集中提取有价值的信息方面显示出非凡的能力。本综述深入探讨了人工智能与多组学的协同作用,强调了人工智能对肿瘤学的革命性影响。我们剖析了这种融合如何重塑癌症研究和临床实践的格局,尤其是在早期检测、诊断、预后、治疗和病理领域。此外,我们还阐述了多组学整合的最新人工智能方法,以便全面了解癌症的复杂生物机制和内在异质性。最后,我们讨论了当前在数据协调、算法可解释性和伦理考虑等方面面临的挑战。应对这些挑战需要多学科合作,为癌症患者提供更精确、个性化和有效的治疗铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-omics based artificial intelligence for cancer research.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

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