title: Using product kernels to predict protein interactions.
abstract: There is a wide variety of experimental methods for the identification of protein interactions. This variety has in turn spurred the development of numerous different computational approaches for modeling and predicting protein interactions. These methods range from detailed structure-based methods capable of operating on only a single pair of proteins at a time to approximate statistical methods capable of making predictions on multiple proteomes simultaneously. In this chapter, we provide a brief discussion of the relative merits of different experimental and computational methods available for identifying protein interactions. Then we focus on the application of our particular (computational) method using Support Vector Machine product kernels. We describe our method in detail and discuss the application of the method for predicting protein-protein interactions, beta-strand interactions, and protein-chemical interactions.
authors.affiliation: Computational Biology, Sandia National Laboratories, PO Box 5800, 87185-1316, Albuquerque, NM 87185-1316, USA. smartin@sandia.gov
authors.forename: Shawn
authors.initials: S
authors.lastname: Martin
authors.forename: W Michael
authors.initials: WM
authors.lastname: Brown
authors.forename: Jean-Loup
authors.initials: JL
authors.lastname: Faulon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*
chemical_list: D020543:Proteome