摘要
论文从蛋白质序列数据的角度出发,通过序列相似度循环匹配构造蛋白质网络,并且通过网络节点重要性排序算法预测蛋白质功能.以节点重要性重要性作为研究对象,在蛋白质网络应用节点重要性算法Page Rank计算网络中蛋白质节点PR值,在Hadoop平台上进行开发实现功能预测的并行计算,减小运行时间.最后通过准确率,召回率以及F1-measure三个指标来衡量结果,并对比传统的功能预测方法,验证结果的有效性.
This paper starts from the perspective of protein sequence data, and constructs the protein network by cyclic sequence similarity matching. Then a novel method based on ranking the importance of network nodes is proposed. Considering the importance of protein nodes in the network, the node importance algorithm Page Rank（PR） is used to compute the nodes＇ PR value. The proposed method is also developed on the Hadoop Platform, which makes it more suitable for huge genome database with great efficiency and parallel computing. Finally, comparing the traditional method of function prediction by the Accurate rate, Recall rate and F1-measure measurements, our method has been validated and the result shows that the method is feasible and valuable for practical usage.
出处
《计算机系统应用》
2016年第5期77-82,共6页
Computer Systems & Applications
基金
福建省自然科学基金(2014J01220)
三明学院科研基金(B201201/G)
福建省教育厅科技基金(JB13187
JA15463)
