Abstract: Disclosed is a method for predicting cancer prognosis, comprising: forming gene pairs by using a plurality of genes to be tested; determining clusters for the formed gene pairs through a clustering method; calculating a distribution of each gene pair based on the determined cluster; and selecting reference gene pairs for determining a class based on the calculated distribution.

Abstract: According to an embodiment of the present invention, it is possible to rapidly and accurately predict a class label of a predetermined test sample by extracting a disease-specific gene pair from gene pairs on a microarray data set representing an expression level for each of genes of a genome and for each of a plurality of samples by considering the correlation in a normal class and the correlation in a disease class, selecting a highest specific gene pair with the highest correlation among the extracted disease-specific genes, and predicting the class label of the predetermined test sample by using the selected highest specific gene pair.

Abstract: A copy number variations detecting apparatus and method according to at least one embodiment of the present invention compare column vectors adjacent to each other on array comparative genomic hybridization data (aCGH data) and compartmentalize the aCGH data into a plurality of segments according to the comparison results, compare row vectors within the segments for each segment and reconfigure the segments into a predetermined number of clusters according to the comparison results, selectively determine the segments as a candidate copy number variation zone corresponding to a distribution form of the clusters for each segment, detect the CNVs within the candidate CNVZ for each sample, and perform merging and pruning on the candidate CNVZ(s) to obtain a final CNVZ(s).