Abstract
BackgroundRecently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity.ResultsWe proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity.ConclusionsPhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.
Highlights
Measuring phenotype similarity began to play an important role in disease diagnosis
Phenotype similarity calculation plays a key role in disease diagnosis
Some methods have been proposed to measure the phenotype similarity. These measures can be grouped into three categories: text mining-based, ontology-based
Summary
Measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. Advances in generation sequencing (NGS) have significantly improved the Mendelian disease diagnosis [1,2,3,4]. Disease diagnosis only using sequence-based approach is still challenging, since lots of diseases have complex phenotypes and high genetic heterogeneity. It is difficult to reveal the relationship between genetic features and complex patient phenotypic features [5]. Phenotype plays an important role in disease diagnosis process. Clinical practice and medical research based on phenotype analysis have drawn great attention in recent years [12,13,14].
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