Improving protein protein interaction prediction based on phylogenetic information using a least-squares support vector machine.
Predicting protein-protein interactions has become a key step of reverse-engineering biological networks to better understand cellular functions. The experimental methods in determining protein-protein interactions are time-consuming and costly, which has motivated vigorous development of computational approaches for predicting protein-protein interactions. A set of recently developed bioinformatics methods utilizes coevolutionary information of the interacting partners (e.g., as exhibited in the form of correlations between distance matrices, where, for each protein, a matrix stores the pairwise distances between the protein and its orthologs in a group of reference genomes). We proposed a novel method to account for the intra-matrix correlations in improving predictive accuracy. The distance matrices for a pair of proteins are transformed and concatenated into a phylogenetic vector. A least-squares support vector machine is trained and tested on pairs of proteins, represented as phylogenetic vectors, whose interactions are known. The intra-matrix correlations are accounted for by introducing a weighted linear kernel, which determines the dot product of two phylogenetic vectors. The performance, measured as receiver operator characteristic (ROC) score in cross-validation experiments, shows significant improvement of our method (ROC score 0.928) over that obtained by Pearson correlations (0.659).
Craig
Roger A
RA
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Liao
Li
L
D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016018:Least-Squares Analysis; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D010802:Phylogeny; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome