Combining SVM and ECOC for Identification of Protein Complexes from Protein Protein Interaction Networks by Integrating Amino Acids' Physical Properties and Complex Topology.

Protein Complexes plays important role in key functional processes in cells by forming Protein Protein Interaction (PPI) networks. Conventionally, they were determined through experimental approaches. For the sake of saving time and cost reduction, many computational methods have been proposed. Fewer computational approaches take into account significant biological information contained within protein amino acid sequence and identified dense sub graphs as complexes from PPI network by considering density and degree statistics. Biological information evaluate the common features for performing a particular biological function among two proteins. Moreover, linear, star and hybrid sub graph structures may be found in PPI network so other topological features of graph are also important. In this article, support vector machine (SVM) in combination with Error-correcting output coding (ECOC) algorithm is utilized to construct an automatic detector for mining multiple protein complexes from PPI network, where amino acid physical properties i.e. kidera factors and a variety of topological constrains are employed as feature vectors. The overall success rates of protein complex identification achieved are 88.6% and 76.0% on MIPS benchmark set by considering DIP and Gavin interactions respectively. Support vector machine was an effective and solid approach for complex detection with amino acid's physical properties and complex topology as dimensional vectors. Error-correcting output coding (ECOC) algorithm is a powerful algorithm for mining multiple protein complexes of small as well as large sizes. The accuracy of complex identification task based on amino acid's physical and complex topological characteristics are strikingly increase when ECOC is integrated with SVM approach. Moreover, this paper implies that ECOC algorithm may succeed over a wide range of applications in biological data mining.