Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Department of Molecular Biology, Columbia University, New York, USA.
Biochim Biophys Acta Proteins Proteom. 2020 Nov;1868(11):140503. doi: 10.1016/j.bbapap.2020.140503. Epub 2020 Jul 22.
As the outward-most representation of life, phenotype is the fundamental basis with which humans understand life and disease. But with the advent of molecular and sequencing technique and research, a growing portion of science research focuses primarily on the molecular level of life. Our understanding in molecular variations and mechanisms can only be fully utilized when they are translated into the phenotypic level. In this study, we constructed similarity network for phenotype ontology, and then applied network analysis methods to discover phenotype/disease clusters. Then, we used machine learning models to predict protein-phenotype associations. Each protein was characterized by the functional profiles of its interaction neighbors on the protein-protein interaction network. Our methods can not only predict protein-phenotype associations, but also reveal the underlying mechanisms from protein to phenotype.
表型作为生命的最外在表现，是人类认识生命和疾病的基础。但随着分子和测序技术及研究的出现，越来越多的科学研究主要集中在生命的分子水平上。只有将对分子变异和机制的理解转化为表型水平，我们才能充分利用它们。在这项研究中，我们构建了表型本体的相似性网络，然后应用网络分析方法来发现表型/疾病聚类。接着，我们使用机器学习模型来预测蛋白质-表型的关联。每个蛋白质的功能特征都由其在蛋白质-蛋白质相互作用网络上的相互作用邻居的功能特征来描述。我们的方法不仅可以预测蛋白质-表型的关联，还可以揭示从蛋白质到表型的潜在机制。
