DNA microarray technology allows to monitor simultaneously the ex- pression levels of thousands of genes during important biological processes and across collections of related experiments. Clustering and classification techniques have proved to be helpful to understand gene function, gene regulation, and cellular processes. However the conventional proximity measures between genes expression data, used for clustering or classification purpose, do not fit gene expression speci- fications as they are based on the closeness of the expression magnitudes regardless of the overall gene expression profile (shape). We propose in this paper an adaptive dissimilarity index which would cover both values and behavior proximity. The ef- fectiveness of the adaptive dissimilarity index is illustrated through a classification process for identification of genes cell cycle phases.