Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences.

ABSTRACT: Compared to the available protein sequences of different organisms, the number of revealed protein-protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cer

SUBMITTER: Guo Y 

PROVIDER: S-EPMC2396404 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature