ArticleBioMed research international2014
Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- Article
- Applications of molecular networks in biomedicine.Biology methods & protocols · 2019Review
- Heterogeneous network embedding for identifying symptom candidate genes.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Factors affecting interactome-based prediction of human genes associated with clinical signs.BMC bioinformatics · 2017Article
- Big data is essential for further development of integrative medicine.Chinese journal of integrative medicine · 2015Article
- Complex networks approach for analyzing the correlation of traditional chinese medicine syndrome evolvement and cardiovascular events in patients with stable coronary heart disease.Evidence-based complementary and alternative medicine : eCAM · 2015Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSymptoms 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.
methodsThis 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.
resultsThe 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.
conclusionsOur study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
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