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• W2113836227 abstract "Abstract Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes. Results: We successfully constructed a three-endophenotype model for Taiwanese hypertensive males with high identification accuracy. Of the three endophenotypes, one is strongly protective, another is weakly protective and the third is highly correlated with developing young-onset male hypertension. Sixteen of the involved 101 genes were highly and consistently influential to the endophenotypes. Identification of SLC4A5, SLC5A10 and LDOC1 indicated that sodium/bicarbonate transport, sodium/glucose transport and cell-proliferation regulation may play important upstream roles and identification of BNIP1, APOBEC3F and LDOC1 suggested that apoptosis, innate immune response and cell-proliferation regulation may play important downstream roles in hypertension. The involved genes not only provide insights into the mechanism of hypertension but should also be considered in future gene mapping endeavors. Availability: Microarray data and test program are available at http://ms.iis.sinica.edu.tw/microarray/index.htm Contact: pan@ibms.sinica.edu.tw or hsu@iis.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online." @default.
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• W2113836227 date "2009-02-23" @default.
• W2113836227 modified "2023-10-17" @default.
• W2113836227 title "A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data" @default.
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• W2113836227 doi "https://doi.org/10.1093/bioinformatics/btp106" @default.
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