Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/156322
40 Visitors42 Hits0 Downloads
Clustering nodes in large-scale biological networks using external memory algorithms
International Conference on Algorithms and Architectures for Parallel Processing (11th : 2011) (24 - 26 October 2011 : Melbourne)
Xiang, Yang; Cuzzocrea, Alfredo; Hobbs, Michael and Zhou, Wanlei. Algorithms and architectures for parallel processing : 11th international conference, ICA3PP 2011, Melbourne, Australia, October 24-26, 2011 : proceedings, p.375-386
Novel analytical techniques have dramatically enhanced our understanding of many application domains including biological networks inferred from gene expression studies. However, there are clear computational challenges associated to the large datasets generated from these studies. The algorithmic solution of some NP-hard combinatorial optimization problems that naturally arise on the analysis of large networks is difficult without specialized computer facilities (i.e. supercomputers). In this work, we address the data clustering problem of large-scale biological networks with a polynomial-time algorithm that uses reasonable computing resources and is limited by the available memory. We have adapted and improved the MSTkNN graph partitioning algorithm and redesigned it to take advantage of external memory (EM) algorithms. We evaluate the scalability and performance of our proposed algorithm on a well-known breast cancer microarray study and its associated dataset.