摘要
蛋白质与蛋白质间的相互作用预测和蛋白质功能预测是后基因组时代生物学中很重要的研究内容。在蛋白质相互作用网络上,人们对这两个问题已经做了大量的研究工作。
本文重点研究分析了蛋白质相互作用网络的基本拓扑属性和假阳性数据在相互作用网络上的拓扑分布特征,同时对蛋白质相互作用预测和蛋白质功能预测的研究进展作了简要的介绍。
基于蛋白质相互作用网络拓扑结构以及假阳性在蛋白质相互作用网络中的分布特征,在相互作用预测算法DC(Defective Clique)的基础上,提出一种新蛋白质相互作用预测算法VTC(Vertex ToClique)。实验结果表明该算法的各项预测性能都要优于DC算法。
针对在蛋白质相互作用网络中,顶点度的分布服从“幂次定律”而且大部分顶点分布在度较低区域的特点,本文提出了一种基于Google搜索技术的蛋白质功能预测算法。并且根据复杂网络的优先连接原则,删除预测后的功能集中大部分的假阳性,提高了预测的可靠性,并通过实验分析验证了该算法的有效性。
Both of the protein-protein interaction (PPI) prediction and the protein function prediction are the important research works in post-genomic era. Based on the PPI networks, there has been a remarkable line of research in the study of those two problems.
Firstly, this paper summarized the research progress about the PPI prediction and protein function prediction. Then it analyzed the topological properties of the PPI networks and the distributed law of the false positives (FPs).
According to the distribution of FPs in the PPI network which seems never considered by previous interaction prediction method, and the topological properties of PPI networks, we proposed an improved interaction prediction algorithm, VTC (Vertex to Clique). Compared with DC (defective clique) algorithm by experiments, VTC algorithm can predict protein interactions with not only higher reliability, but also larger quantity.
In the PPI networks, the degree distribution follows a power law distribution. Moreover, the degree of most vertices is low. According to those characteristics, we have proposed a novel protein function prediction algorithm based on the Google search engine technology. In our method, we also utilize the preferential attachment criteria to improve the prediction reliability. Applied our method to S.cerevisiae PPI dataset, the experimental results have shown that our method has a high prediction performance.
引文
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