| 摘要 |
The disclosed subject matter provides techniques for multiple-factor selection. The factors can be features or elements that are jointly associated with one or more outcomes by their joint presence or absence. There may be a non-causative correlation between the factors, features, or elements and the outcomes. In some embodiments, Entropy Minimization and Boolean Parsimony (EMBP) is used to identify modules of genes jointly associated with disease from gene expression data, and a logic function is provided to connect the combined expression levels in each gene module with the presence of disease. The smallest module of genes whose joint expression levels can predict the presence of disease can be identified. |
