Identifying oncogenes as features for clinical cancer prognosis by Bayesian nonparametric variable selection algorithm
作者: Huijun WangLiqiu HuangRunyu JingYongning YangKeqin LiuMenglong LiZhining Wen
作者单位: 1College of Chemistry, Sichuan University, Chengdu 610064, PR China
刊名: Chemometrics and Intelligent Laboratory Systems, 2015, Vol.146 , pp.464-471
来源数据库: Elsevier Journal
DOI: 10.1016/j.chemolab.2015.07.004
关键词: Bayesian nonparametric variable selectionDNA microarraysOncogenesSupport vector machineCancer prognosis
原始语种摘要: Abstract(#br)In clinical research, DNA microarrays are widely applied in the identification of the oncogenes, which are differentially expressed between two clinical states and considered as predictors for the cancer prognosis. Due to the heterogeneity of clinical samples, the differentially expressed genes (DEGs) discovered by current statistical methods or machine learning algorithms involve a number of genes unrelated to the phenotypic differences between the compared samples and, consequently, will impact on the reliability of the predictive models in the cancer prognosis. In our study, we proposed Bayesian nonparametric variable selection algorithm, a stochastic random and hierarchical search method, to separate out the cancer-related genes from the DEG lists. The importance of the...
全文获取路径: Elsevier  (合作)
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