A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

中文翻译：

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疾病分类：从表型相似性到综合基因组学及其他。

现代生物医学研究的一个基本挑战是了解在表型水平上相似的疾病在分子水平上如何相似。各种基因组数据集与传统使用的表型疾病相似性的整合揭示了新颖的遗传和分子机制，并模糊了单基因（孟德尔）和复杂疾病之间的区别。基于网络的医学已经成为一种补充方法，用于识别引起疾病的基因，遗传介体，基本细胞功能的破坏以及药物重新定位。机器和深度学习方法的最新发展允许利用有关疾病的现实生活信息来完善遗传和表型疾病的关系。