Evaluation of gene-expression clustering via mutual information distance measure.

Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient.

Results: Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions.