Abstract:
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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Peng J,Hui W,Shang Xdoi
10.1186/s12859-018-2102-9subject
Has Abstractpub_date
2018-04-11 00:00:00pages
114issue
Suppl 5issn
1471-2105pii
10.1186/s12859-018-2102-9journal_volume
19pub_type
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