title: Prediction of protein-protein interactions from primary sequences.
abstract: The prediction of protein-protein interactions is a difficult problem in biology. In this study, an efficient method is presented to predict protein-protein interactions with sequence composition information. Four kinds of basic building blocks of protein sequences are investigated. The experimental results show that there is minor difference in prediction performance among the four kinds of different building blocks. The method based on combination of all building blocks out performs any of the building blocks. We also demonstrate that the use of Latent Semantic Analysis (LSA) can efficiently remove noise and improve the prediction efficiency without significantly degrading the performance.
authors.affiliation: Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China. qwdong@fudan.edu.cn
authors.forename: Qiwen
authors.initials: Q
authors.lastname: Dong
authors.forename: Shuigeng
authors.initials: S
authors.lastname: Zhou
authors.forename: Xuan
authors.initials: X
authors.lastname: Liu
mesh_terms: D001665:Binding Sites; D030562:Databases, Protein; D010363:Pattern Recognition, Automated; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins