Computational systems biology methods today help life scientists with exploring the masses of available data. Different methods and tools have been developed in an attempt to serve the need of extraction, exploration, and deeper analysis of the data. For instance, statistical and machine learning methods have been successfully used in identifying pattern signatures. However, these methods fail to give deeper insights into gene expression data.
Here, we introduce Key Pathway Miner, or KPM, that allows extracting and visualizing sub-pathways that may be of interest given the results of a series of gene expression studies. We aim to detect "highly-connected" sub-networks where most genes show "similar" expression behavior. In particular, given network and gene expression study data, those maximal sub-networks are identified where all but n nodes of the network are expressed similarly on all but m cases of the user-specified gene expression study data. As finding such modules is computationally intense, we developed and implemented heuristics algorithms based on Ant Colony Optimization.

Alcaraz NM, Kucuk H, Weile J, Wipat A, Baumbach J (2011) KeyPathwayMiner - Detecting case-specific biological pathways by using expression data. Int Math. 2011, 7:4, 299-313.