Biclustering has been largely applied for gene expression data analysis. In recent years, a clearer understanding of the synergies between pattern mining and biclustering gave rise to a new class of biclustering algorithms, referred as pattern-based biclustering. These algorithms are able to discover exhaustive structures of biclusters with flexible coherency and quality. Background knowledge has also been increasingly applied for biological data analysis to guarantee relevant results. In this context, despite numerous contributions from domain-driven pattern mining, there is not yet a solid view on whether and how background knowledge can be applied to guide pattern-based biclustering tasks. In this work, we extend pattern-based biclustering algorithms to effectively seize efficiency gains in the presence of constraints. Furthermore, we illustrate how constraints with succinct, (anti-)monotone and convertible properties can be derived from knowledge repositories and user expectations. Experimental results show the importance of incorporating background knowledge within pattern-based biclustering to foster efficiency and guarantee non-trivial yet biologically relevant solutions.
展开▼
