Effect of the quality of the interaction data on predicting protein function from protein-protein interactions.
Protein function prediction is an important issue in the post-genomic era. When protein function is deduced from protein interaction data, the traditional methods treat each interaction sample equally, where the qualities of the interaction samples are seldom taken into account. In this paper, we investigate the effect of the quality of protein-protein interaction data on predicting protein function. Moreover, two improved methods, weight neighbour counting method (WNC) and weight chi-square method (WCHI), are proposed by considering the quality of interaction samples with the neighbour counting method (NC) and chi-square method (CHI). Experimental results have shown that the qualities of interaction samples affect the performances of protein function prediction methods seriously. It is also demonstrated that WNC and WCHI methods outperform NC and CHI methods in protein function prediction when example weights are chosen properly.
Ni
Qing-Shan
QS
College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, Hunan, 410073, China. niqingshan@nudt.edu.cn
Wang
Zheng-Zhi
ZZ
Li
Gang-Guo
GG
Wang
Guang-Yun
GY
Zhao
Ying-Jie
YJ
D016009:Chi-Square Distribution; D030562:Databases, Protein / Q000592:standards*; D025941:Protein Interaction Mapping; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
D029701:Saccharomyces cerevisiae Proteins