Selection of GO-Based semantic similarity measures through AMDE for predicting protein-protein interactions

Authors:
Anirban Mukhopadhyay;Moumita De;Ujjwal Maulik
Affiliations:
Department of Computer Science and Engineering, University of Kalyani, Kalyani, India;Department of Computer Science and Engineering, University of Kalyani, Kalyani, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Year:
2011

Citing 5
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Abstract

Protein-protein interactions (PPI) form the core part of the entire interatomic system for all the living elements. In this article, the role of different Gene Ontology(GO)-based semantic similarity measures in predicting PPIs have been explored. To find out a relevant subset of semantic similarity measures, a feature selection approach is developed with Angle Modulated Differential Evolution(AMDE), an improved binary differential evolution technique. In this feature selection approach, SVM classifier is used as a wrapper where different metrics like sensitivity, specificity accuracy and Area Under Curve (AUC) are measured to find the best performing feature subset. Results have been demonstrated for real-life PPI data of yeast.