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dc.contributor.authorLi, X.
dc.contributor.authorZhou, X.
dc.contributor.authorPeng, Yonghong
dc.contributor.authorLiu, B.
dc.contributor.authorZhang, R.
dc.contributor.authorHu, J.
dc.contributor.authorYu, J.
dc.contributor.authorJia, C.
dc.contributor.authorSun, C.
dc.date.accessioned2016-11-28T15:10:29Z
dc.date.available2016-11-28T15:10:29Z
dc.date.issued2014
dc.identifier.citationLi X, Zhou X, Peng Y et al (2014) Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes. Biomed Research International. 2014: 435853.
dc.identifier.urihttp://hdl.handle.net/10454/10724
dc.descriptionYes
dc.description.abstractSymptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
dc.description.sponsorshipNSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.en_US
dc.relation.isreferencedbyhttps://doi.org/10.1155/2014/435853
dc.rights© 2014 Xing Li et al. This is an open access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/3.0/)
dc.subjectAlgorithms
dc.subject; Computational biology
dc.subject; Gene regulatory networks
dc.subject; Genetic association studies
dc.subject; Genome-wide association study
dc.subject; Humans
dc.subject; Traditional Chinese medicine
dc.subject; PubMed
dc.titleNetwork based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes
dc.status.refereedYes
dc.typeArticle
dc.type.versionPublished version
refterms.dateFOA2018-07-26T09:18:02Z


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