Proposing on Optimized Homolographic Motif Mining Strategy Based on Parallel Computing for Complex Biological Networks Publisher



Alinejadrokny H^{1, 2, 3} Show Affiliations
Source: Journal of Medical Imaging and Health Informatics Published:2016

Abstract

Existing big datasets of biological data brings a big challenge for the traditional computational algorithms. To have a better understanding of complex biological networks and existing relationships among the components, network models have been using for a long time. Complex networks from diverse scopes like Sociology or Biology exist in many e-science data sets. Mostly we need computational algorithms for dealing with networks; for example, detecting characteristic patterns, network motifs or problems involving sub-graph mining and graph isomorphism. In this paper, we studied previous strategies for motif discovery in biological networks and then proposed a new strategy based on an optimization method and parallel computing. The proposed method is implemented on different networks and is compared with current strategies. The results, showed that the proposed method works very well and increases the speed of biological networks mining. © Copyright 2016 American Scientific Publishers. All rights reserved.