The local protein-protein interactional feature can be caught by machine-learning method
Protein-protein interactions (PPIs) are central for most biological processes. Much effort has been put into the development of methodology for predicting PPIs and the construction of PPIs networks. Though a high accurate rate those methods have achieved, it was found that the accurate rate strongly depends on the balance of datasets. Compare to negative datasets, positive datasets contain some proteins which are called hub protein intact more proteins. And the unbalance between datasets leads to an excellent performance of PPIs prediction. But when one used balance datasets, the performance is disappointed. Different Biological functions are supported by different local PPIs network. Does it mean that local PPIs network has its own feature? In this paper, we managed to catch features of local networks in three species on the condition that there is no unbalance between positive dataset and negative dataset. Features of local PPIs network fades as the network extended. The associate rules method is used to analyze features of local PPI network. All the datasets used in this study are derived from public available database.
protein-protein interaction interactional feature machine-learning method balanced random sampling associate rules
Tienan Feng Dingli Jin Liang Da Yifei Wang
Department of Mathematics Shanghai University Shanghai 200444, China Shanghai Institutes for Biological Sciences Institute of Biochemistry and Cell Biology Shanghai 2000
国际会议
上海
英文
1681-1685
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）