paraSNF: An Parallel Approach for Large-Scale Similarity Network Fusion

Abstract

With the rapid accumulation of multi-dimensional disease data, the integration of multiple similarity networks is essential for understanding the development of diseases and identifying subtypes of diseases. The recent computational efficient method named SNF is suitable for the integration of similarity networks and has been extensively applied to the bioinformatics analysis. However, the computational complexity and space complexity of the SNF method increases with the increase of the sample numbers. In this research, we develop a parallel SNF algorithm named paraSNF to improve the speed and scalability of the SNF. The experimental results on two large-scale simulation datasets reveal that the paraSNF algorithm is 30x–100x faster than the serial SNF. And the speedup of the paraSNF over the SNF which running on multi-cores with multi-threads is 8x–15x. Furthermore, more than 60% memory space are saved using paraSNF, which can greatly improve the scalability of the SNF.