BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

Abstract

Biological network represents the interaction or relationship between the biological entities such as proteins and genes of a biological process. A biological network with thousands of millions of vertices makes its processing complex and challenging. In this article, we have proposed a novel Bloom Filter for biological networks, called BionetBF, to provide fast membership identification of the biological network edges or paired biological data. BionetBF is capable of executing millions of operations within a second on datasets having millions of paired biological data while occupying tiny amount of main memory. We have conducted rigorous experiments to prove the performance of BionetBF with large datasets. The experiment is conducted using 12 generated datasets and three biological network datasets. BionetBF demonstrates higher performance while maintaining a 0.001 false positive probability. BionetBF is also compared with other filters: Cuckoo Filter and Libbloom, where BionetBF proves its supremacy by exhibiting higher performance with a smaller sized memory compared with large sized filters of Cuckoo Filter and Libbloom.