Predicting Protein Function Based on Bayesian Network and Protein Interaction Reliability
Jiyang Zhang · Jiguang shengwu xuebao · 2009
Function annotation for proteins is one of the most important problems in the post-genomic era,and the protein-protein interaction data are employed by many researchers to assign functions to proteins.In this paper,a new method is developed to predict protein functions,which is based on Bayesian network and protein interaction reliability.The proposed algorithm constructs a Bayesian network model to assign functions to the unannotated protein and takes the reliability of protein interactions into account.The results,which is obtained by 3-fold cross-validation test on the data set constructed for Saccharomyces,show that the performance of protein function prediction can be improved by using interaction reliability,moreover,the proposed method outperforms some existing approaches and can be used to obtain desirable results for protein function prediction.