BFRM has been heavily motivated by problems of gene expression analysis, especially in problems of biological pathway analysis and phenotyping in cancer genomics. The framework is relevant in many exploratory and predictive gene expression studies, including problems of prediction in which factor analysis provides an automatic and statistically formal approach to identification of underlying "metagene" structure relevant to predictive phenotyping, and the core problems of evaluating and identifying substructure in expression data related to components of complex biological networks of intersecting pathways. The software additionally includes model components that allow for the within-analysis and automatic handling of data issues (generalized normalisation and assay artifact correction) arising in all expression studies that combine data on microarrays across experimental conditions or laboratories.

Examples will be provided from the Duke Integrated Cancer Biology Program (Duke ICBP) that studies complex networks of intersecting biological pathways in cancer genomics. The ICBP is a program of the NCI, and BFRM has been developed under partial support through the ICBP from NCI. The applications developments have heavily influenced the evolution of the software and Duke collaborators Joseph Nevins and Jeff Chang have been particularly influential in providing continued stimulus and suggestions.