Protein complex prediction via cost-based clustering
Bioinformatics
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Proteins interact among them and different interactions form a very huge number of possible combinations representable as protein to protein interaction (PPI) networks that are mapped into graph structures. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties, starting from a set of known proteins interacting among each other. For example, predicting the configuration of a subset of nodes in a graph (representing a PPI network), allows to study the generation of protein complexes. Nevertheless, due to the huge number of possible configurations of protein interactions, automatic based computation tools are required. Available prediction tools are able to analyze and predict possible combinations of proteins in a PPI network which have biological meanings. Once obtained, the protein interactions are analyzed with respect to biological meanings representing quality measures. Nevertheless, such tools strictly depend on input configuration and require biological validation. In this paper we propose a new prediction tool based on integration of different prediction results obtained from available tools. The proposed integration approach has been implemented in an on line available tool, IMPRECO standing for IMproving PREdiction of COmplexes. IMPRECO has been tested on publicly available datasets, with satisfiable results.