The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields—Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy—are discussed.

中文翻译：

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精准医学的生物医学大数据技术、应用和挑战：综述

生物医学大数据的爆炸性增长为精准医学的知识发现和转化应用领域带来了重大机遇和挑战。大数据的有效管理、分析和解释可以为精准医学的突破性进步铺平道路。然而，大规模分子和临床数据的自动化收集方面取得的前所未有的进步也给数据分析和解释带来了巨大的挑战，需要开发新的计算方法。一些潜在的挑战包括维度灾难、数据异构性、数据缺失、类别不平衡和可扩展性问题。本文概述文章重点介绍大数据在精准医疗中应用的最新进展和突破。总结了关键方面，包括内容、数据源、技术、工具、挑战和现有差距。讨论了数据仓库和数据管理、电子病历、生物医学成像信息学、人工智能辅助手术设计和手术优化、组学数据、健康监测数据、知识图谱、公共卫生信息学以及安全和隐私九个领域。