In targeted cancer therapy, great relevance is assumed by data-driven investigations
on the fundamental mechanisms by which epigenetic modifications cooperate
to regulate the transcriptional status of genes. At the high resolution level of
genome-wide studies, only general, mean regulative motifs are drawn, with possible
multi-functional co-regulative roles remaining concealed. In order to retrieve sharper
and more reliable regulative patterns, in this work we propose the application of Kplane
regression to partition the set of protein coding genes into clusters with shared
regulative mechanisms. Completely data-driven, the approach has computed clusters of
genes significantly better fitted by specific linear models than by single regression, and
characterized by distinct histonic input patterns and mean measured expression values
