A computer-implemented method of identifying a biological signature for a disease state, comprising: (a) receiving a plurality of data sets, each data set comprising an expression level for a plurality of genes in a biological system in different states comprising a disease state and a control state; (b) for each of a plurality of iterations: (i) splitting the plurality of data sets into a training portion and a test portion, (ii) using the training portion of the plurality of data sets to select, as a sub-candidate signature, a predetermined number of genes in the training portion with differential expressions above a threshold and to generate a classification rule that assigns each of the data sets to one of a disease class and a control class, and (iii) using the test portion of the plurality of data sets to apply the classification rule to assign each data set to one of the disease class and the control class and generate a performance sub-measure associated with the sub-candidate signature for the classification rule based on the assignment; (c) generating a candidate signature having the predetermined number of genes by calculating, for each gene in a set union of all of the genes included in the sub-candidate signatures having the predetermined number of genes, a weighted frequency and selecting frequently-identified genes based on the weighted frequency; the weighted frequency based on; (i) an appearance of the gene in the selected sub-candidate signature having the predetermined number of genes, and (ii) the generated performance sub-measure; (d) generating a performance measure associated with the candidate signature from a composite of the performance sub-measures; (e) repeating steps (b) through (d) for a plurality of different values of the predetermined number to generate a plurality of candidate signatures and a plurality of associated performance measures; and (f) storing, as the biological signature, the candidate signature associated with a highest performance measure. (a) receiving a plurality of data sets, each data set comprising an expression level for a plurality of genes in a biological system in different states comprising a disease state and a control state; (b) for each of a plurality of iterations: (i) splitting the plurality of data sets into a training portion and a test portion, (ii) using the training portion of the plurality of data sets to select, as a sub-candidate signature, a predetermined number of genes in the training portion with differential expressions above a threshold and to generate a classification rule that assigns each of the data sets to one of a disease class and a control class, and (iii) using the test portion of the plurality of data sets to apply the classification rule to assign each data set to one of the disease class and the control class and generate a performance sub-measure associated with the sub-candidate signature for the classification rule based on the assignment; (i) splitting the plurality of data sets into a training portion and a test portion, (ii) using the training portion of the plurality of data sets to select, as a sub-candidate signature, a predetermined number of genes in the training portion with differential expressions above a threshold and to generate a classification rule that assigns each of the data sets to one of a disease class and a control class, and (iii) using the test portion of the plurality of data sets to apply the classification rule to assign each data set to one of the disease class and the control class and generate a performance sub-measure associated with the sub-candidate signature for the classification rule based on the assignment; (c) generating a candidate signature having the predetermined number of genes by calculating, for each gene in a set union of all of the genes included in the sub-candidate signatures having the predetermined number of genes, a weighted frequency and selecting frequently-identified genes based on the weighted frequency; the weighted frequency based on; (i) an appearance of the gene in the selected sub-candidate signature having the predetermined number of genes, and (ii) the generated performance sub-measure; (i) an appearance of the gene in the selected sub-candidate signature having the predetermined number of genes, and (ii) the generated performance sub-measure; (d) generating a performance measure associated with the candidate signature from a composite of the performance sub-measures; (e) repeating steps (b) through (d) for a plurality of different values of the predetermined number to generate a plurality of candidate signatures and a plurality of associated performance measures; and (f) storing, as the biological signature, the candidate signature associated with a highest performance measure.