Genes work in concert as a system as opposed to independent entities and mediate disease states. There has been considerable interest in understanding variations in molecular signatures between normal and disease states. However, a majority of techniques implicitly assume homogeneity between samples within a given group and use a fixed set of genes in discerning the groups. The proposed study overcomes these caveats by using a selective-voting convex-hull ensemble procedure that accommodates molecular heterogeneity within and between groups. The significance of the study is its potential to selectively retrieve sample-specific ensemble sets and investigate variations in the networks corresponding to the ensemble set across these samples. These characteristics fit well within the scope of personalized medicine and comparative effectiveness research that emphasize on patient-tailored interventions. While the results are demonstrated on colon cancer gene expression profiles the approach as such is generic and can be readily extended to other settings.