title: Large-scale Protein-Protein Interaction prediction using novel kernel methods.
abstract: Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.
authors.affiliation: Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu
authors.forename: Xue-Wen
authors.initials: XW
authors.lastname: Chen
authors.forename: Bing
authors.initials: B
authors.lastname: Han
authors.forename: Jianwen
authors.initials: J
authors.lastname: Fang
authors.forename: Ryan J
authors.initials: RJ
authors.lastname: Haasl