Metabolic networks classification and knowledge discovery by information granulation. (February 2020) - Martino, Alessio; Giuliani, Alessandro; Todde, Virginia; Bizzarri, Mariano; Rizzi, Antonello
Highlights: Novel information granulation and knowledge discovery technique for structured data. Information granules as relevant substructures (high sensitivity and specificity). Information granules are further filtered at classification stage (suboptimal subset). Tests on real metabolic pathways data classification show remarkable performances. Knowledge discovery over filtered granules paves the way for further studies. Abstract: Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges. The proposed classification system has been tested on the entire set of organisms (5299) for which metabolic networks are known, allowing for both a perfect mirroring of the underlying taxonomy and the identification of most discriminant metabolic reactions and pathways. The widespread diffusion of graph (network) structures in biology makes the proposed pattern recognition approach potentially very useful in many different fields of application. More specifically, the possibility to have a reliable metric to compare different metabolic systems is instrumental in emerging fields like microbiome analysis and, more in general, for proposing metabolic networks as a universal phenotype spanning the entire tree of life and in direct contact with environmental cues.