近年来，随着临床研究、基因组学、蛋白质组学和公共卫生记录等各个领域的显着扩展，出现了“癌症大数据”的理念。组学技术的进步正在为生物医学和疾病诊断中的癌症大数据做出重大贡献。广泛的癌症大数据的日益可用性为多模式人工智能（AI）框架的开发奠定了基础。这些框架旨在分析高维多组学数据，提取难以手动获取的有意义的信息。尽管可解释性和数据质量仍然是严峻的挑战，但这些方法对于增进我们对癌症生物学的理解以及改善患者护理和临床结果具有巨大的希望。在这里，我们概述了癌症大数据，并探讨了传统机器学习和深度学习方法在癌症基因组和蛋白质组研究中的应用。我们简要讨论了人工智能技术在组学数据综合分析中的挑战和潜力，以及癌症个性化治疗方案的未来方向。© 2023 John Wiley

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.© 2023 John Wiley & Sons Ltd.