Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings.

中文翻译：

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患者分层的网络医学：从单层到多组学

精准医学研究越来越依赖于多种类型组学的综合分析。在大数据时代，不同健康相关信息的大量可用性代表着巨大但同时尚未开发的机会，在疾病的预防、诊断和预后方面具有潜在的根本性作用。需要计算方法来结合这些数据以创建给定疾病的全面视图。网络科学可以根据不同性质的分子参与者之间的关系对生物医学数据进行建模，并已成功提出作为研究人类疾病的新范式。患者分层是一项公开挑战，旨在识别具有不同疾病表现、严重程度和预期生存时间的亚型。几种基于高通量基因表达测量的分层方法已被成功应用。然而，很少有人尝试利用各种基因型和表型数据的整合来发现新的亚型或改进已知分组的检测。