发明名称 |
A STOCHASTIC VARIABLE SELECTION METHOD FOR MODEL SELECTION |
摘要 |
A method of identifying differentially-expressed genes includes deriving an analysis of variance (ANOVA) or analysis of covariance (ANCOVA) model for expression data associated with a number of genes; from the ANOVA or ANCOVA model, deriving a linear regression model defined at least in part by an observation vector representative of an observed subset of the gene-expression data, a design matrix of regressor variables, a vector of regression coefficients representing gene contribution to the observation vector, and a measurement error vector; and to the linear regression model, applying a hierarchical selection algorithm to designate a subset of the regression coefficients as significant regression coefficients, the selection algorithm representing at least one of the observation vector, the design matrix, and the measurement error vector as being hierarchically dependent on parameters having predetermined probabilistic properties, wherein the designated subset corresponds to a respective subset of the genes identified as differentially expressed. |
申请公布号 |
WO2004109540(A2) |
申请公布日期 |
2004.12.16 |
申请号 |
WO2004US17504 |
申请日期 |
2004.06.01 |
申请人 |
CASE WESTERN RESERVE UNIVERSITY;THE CLEVELAND CLINIC FOUNDATION;RAO, JONNAGADDA, SUNIL;ISHWARAN, HEMANT |
发明人 |
RAO, JONNAGADDA, SUNIL;ISHWARAN, HEMANT |
分类号 |
G01N33/48;G01N33/50;G06F19/20 |
主分类号 |
G01N33/48 |
代理机构 |
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主权项 |
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地址 |
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