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.

作为生命的最外层代表，表型是人类了解生命和疾病的基本基础。但是随着分子和测序技术以及研究的出现，越来越多的科学研究主要集中在分子的生命水平上。我们对分子变异和机制的理解只有在将其转化为表型时才能充分利用。在这项研究中，我们为表型本体构建了相似性网络，然后应用网络分析方法来发现表型/疾病簇。然后，我们使用机器学习模型来预测蛋白质表型的关联。每种蛋白质的特征是在蛋白质-蛋白质相互作用网络上其相互作用邻居的功能概况。我们的方法不仅可以预测蛋白质表型的关联，