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基于多基因组合选择模型的结肠癌特征基因选取 被引量：1

Informative Genes Selection of Colon Cancer Based on Polygenic Combination Selection Model
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摘要 通过基因的Bhattacharyya距离指标过滤掉大部分无关基因,然后探索性的提出了一种建立多基因组合选择模型的统计方法。从候选特征基因中选取了8个可能的结肠癌特征基因集合,判别分析的结果证明了该方法的可行性。 To select informative genes of colon cancer by analysis of gene expression. Most irrelevant gene filtration by the gene distance method of Bhattacharyya, then putting forward a statistical method of establishing polygenic combination selection model. The study selects 8 possible informative genes sets of colon cancer from the candidate informative genes. The results of discriminant analysis show the feasibility of this approach, and play a certain reference arriving role to clinical diagnosis of cancer and research in biomedical sciences.
作者 马超
机构地区 南京大学公共卫生管理与医疗保障政策研究中心
出处 《统计与信息论坛》 CSSCI 2012年第6期78-82,共5页 Journal of Statistics and Information
关键词 基因表达谱 生物信息学 多基因组合选择模型 结肠癌 DNA microarray Bioinformatics polygenic combination selection model colon cancer
分类号 Q16 [生物学—普通生物学] TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献12

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共引文献36

同被引文献10

  • 1Huang J,Breheny P,Ma S.A Selective Review of Group Selection in High Dimensional Models[J].Statistical Science,2012,27(4).
  • 2Fan J,Lv J.Sure Independence Screening for Ultrahigh Dimensional Feature Space[J].Journal of the Royal Statistical Society,Ser.B,2008,70(5).
  • 3Hall P,Miller H.Using Generalized Correlation to Effect Variable Selection in Very High Dimensional Problems[J].Journal of Computational and Graphical Statistics,2009,18(3).
  • 4Fan J,Samworth R,Wu Y.Ultrahigh Dimensional Feature Selection:Beyond the Linear Model[J].Journal of Machine Learning Research,2009(10).
  • 5Fan J,Song R.Sure Independence Screening in Generalized Linear Models With NP-Dimensionality[J].The Annals of Statistics,2010,38(6).
  • 6Fan J,Feng Y,Song R.Nonparametric Independence Screening in Sparse Ultra-high-dimensional Additive Models[J].Journal of the American Statistical Association,2011,106(494).
  • 7Fan J,Ma Y,Dai W.Nonparametric Independence Screening in Sparse Ultra-high-dimensional Varying Coefficient Models[J].Journal of the American Statistical Association,2014,109(507).
  • 8Liu J,Li R,Wu S.Feature Selection for Varying Coefficient Models with Ultrahigh-dimensional Covariates[J].Journal of the American Statistical Association,2014,109(505).
  • 9Segal M,Dahlquist D,Conklin B.Regression Approach for Microarray Data Analysis[J].Journal of Computational Biology,2003,10(6).
  • 10Li R,Wei Z,Zhu L.Feature Screening Via Distance Correlation Learning[J].Journal of the American Statistical Association,2012,107(499).

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二级引证文献10

统计与信息论坛
2012年 第6期

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