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Lili Wei Leixi Cao Yanyan Miao Shuju Wu Shumei Xu Ruisheng Wang Jun Du Aihua Liang Yuejun Fu 《Biotechnology letters》2017,39(8):1129-1139
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Peter A. Larsen R. Alan Harris Yue Liu Shwetha C. Murali C. Ryan Campbell Adam D. Brown Beth A. Sullivan Jennifer Shelton Susan J. Brown Muthuswamy Raveendran Olga Dudchenko Ido Machol Neva C. Durand Muhammad S. Shamim Erez Lieberman Aiden Donna M. Muzny Richard A. Gibbs Anne D. Yoder Jeffrey Rogers Kim C. Worley 《BMC biology》2017,15(1):110
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Background
Recently, 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.Results
We 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.Conclusions
PhenoNet 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.18.
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