计算机科学
比例(比率)
集合(抽象数据类型)
吞吐量
任务(项目管理)
并行计算
数据挖掘
分布式计算
机器学习
量子力学
电信
物理
经济
管理
程序设计语言
无线
作者
Lun Hu,Xiaohui Yuan,Pengwei Hu,Kwong Chan
标识
DOI:10.1016/j.compbiolchem.2017.03.009
摘要
With a rapid development of high-throughput genomic technologies, a vast amount of protein-protein interactions (PPIs) data has been generated for difference species. However, such set of PPIs is rather small when compared with all possible PPIs. Hence, there is a necessity to specifically develop computational algorithms for large-scale PPI prediction. In response to this need, we propose a parallel algorithm, namely pVLASPD, to perform the prediction task in a distributed manner. In particular, pVLASPD was modified based on the VLASPD algorithm for the purpose of improving the efficiency of VLASPD while maintaining a comparable effectiveness. To do so, we first analyzed VLASPD step by step to identify the places that caused the bottlenecks of efficiency. After that, pVLASPD was developed by parallelizing those inefficient places with the framework of MapReduce. The extensive experimental results demonstrate the promising performance of pVLASPD when applied to prediction of large-scale PPIs.
科研通智能强力驱动
Strongly Powered by AbleSci AI