Computational Analysis of the Synergy Among Multiple Interacting Factors

Abstract

A method is provided for selecting two or more genes from gene expression data. In the method, gene expression data for a plurality of genes is provided, where the gene expression data include expression levels for each of the plurality of genes. The gene expression data is discretized. Based on the discretized gene expression data, the synergy among the plurality of genes with respect to a phenotype, for example, presence or absence of a disease in a tissue, is evaluated. Two or more genes whose synergy exceeds a predetermined threshold are selected. A system implementing the method is also provided.

1 . A method for selecting factors from a data set of measurements, the measurements including values of the factors and outcomes, comprising:
 identifying two or more factors that are jointly associated with one or more outcomes from the data set; and   analyzing each of the two or more factors to determine at least one cooperative interaction among the factors with respect to an outcome.   
     
     
         2 . The method of  claim 1 , wherein the two or more factors comprise a module of factors. 
     
     
         3 . The method of  claim 1 , wherein the two or more factors comprise a sub-module of factors. 
     
     
         4 . The method of  claim 1 , wherein the at least one interaction comprises a structure of interactions. 
     
     
         5 . The method of  claim 1 , wherein the two or more factors comprise two or more genes, the data includes gene expression data comprising expression levels for each of the two or more genes, and the one or more outcomes includes presence or absence of a disease. 
     
     
         6 . The method of  claim 5 , wherein the two or more genes comprise a module of genes. 
     
     
         7 . The method of  claim 6 , wherein the module of genes comprise a smallest cooperative module of genes with joint expression levels that can be used for a prediction of the presence of disease. 
     
     
         8 - 16 . (canceled) 
     
     
         17 . A system for selecting factors from a data set of measurements, each measurement comprising values of the factors and outcomes comprising:
 at least one processor, and   a computer readable medium coupled to the at least one processor, having stored thereon instructions which when executed cause the at least one processor to:
 identify two or more factors that are jointly associated with one or more outcomes or factors from the data; and 
 analyze each of the two or more factors to determine at least one cooperative interaction among the factors with respect to an outcome or factor. 
   
     
     
         18 . The system of  claim 17 , wherein the two or more factors comprise a module of factors. 
     
     
         19 . The system of  claim 18 , wherein the module of factors comprises at least one sub-module of factors. 
     
     
         20 . The system of  claim 18 , wherein the at least one cooperative interaction comprises a structure of interactions. 
     
     
         21 . The system of  claim 20 , wherein the at least one cooperative interaction comprises a logic function. 
     
     
         22 . The system of  claim 21 , wherein the two or more factors comprise two or more genes, the data comprises gene expression data comprising expression levels for each of the two or more, and the one or more outcomes comprise presence or absence of a disease. 
     
     
         23 . The system of  claim 22 , wherein the two or more genes comprise a module of genes. 
     
     
         24 . The system of  claim 23 , wherein the module of genes comprises at least one sub-module of genes. 
     
     
         25 . The system of  claim 23 , wherein the module of genes comprises a smallest module of genes with joint expression levels that can be used for a prediction of the presence of disease with high accuracy.