Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices

Background: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes. Results: We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represe...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Roger A. Craig, Li Liao
• Keywords: BMCBI 2007, Distance Matrices, Intermediate Distance Matrices, Protein-protein Interactions