國家衛生研究院 NHRI:Item 3990099045/6416
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/6416


    Title: A two-stage random forest-based pathway analysis method
    Authors: Chung, RH;Chen, YE
    Contributors: Division of Biostatistics and Bioinformatics
    Abstract: Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS) data that may still yield new results from these valuable datasets. Most of the current pathway analysis methods focused on testing the cumulative main effects of genes in a pathway. However, for complex diseases, gene-gene interactions are expected to play a critical role in disease etiology. We extended a random forest-based method for pathway analysis by incorporating a two-stage design. We used simulations to verify that the proposed method has the correct type I error rates. We also used simulations to show that the method is more powerful than the original random forest-based pathway approach and the set-based test implemented in PLINK in the presence of gene-gene interactions. Finally, we applied the method to a breast cancer GWAS dataset and a lung cancer GWAS dataset and interesting pathways were identified that have implications for breast and lung cancers.
    Date: 2012-05
    Relation: PLoS ONE. 2012 May;7(5):Article number e36662.
    Link to: http://dx.doi.org/10.1371/journal.pone.0036662
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1932-6203&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000305335000049
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84860631518
    Appears in Collections:[Ren-Hua Chung] Periodical Articles

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