IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel methods for predicting protein--protein interactions
Bioinformatics
Predicting types of protein-protein interactions using a multiple-instance learning model
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Using decision templates to predict subcellular localization of protein
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Protein-protein interactions (PPIs) play a key role in many cellular processes, such as the regulation of enzymes, signal transduction or mediating the adhesion of cells. Knowing about the multitude of PPIs that allow the cell to function can help the biological scientist understand the molecular machinery of the cell. Unfortunately, it is both time-consuming and expensive to do so solely based on experiments due to the nature of the problem whose complexity is obviously overwhelming, just like the fact that ''life is complicated''. Therefore, developing computational approaches for predicting PPIs, binding sites and interaction types would be of significant value in this regard. In this paper, we propose a novel method for predicting the types of PPIs based on decision templates. First, we introduce the concept of the tensor product to construct three kinds of feature vectors which are the amino acid composition tensor product, the residue multi-scale conservation energy tensor product and the secondary structure content tensor product, then the correlation-based feature selection method was also introduced to reduce the dimensionality of these feature vectors. So, the protein pair can be represented by our three new kinds of feature vectors and Zhu's six kinds of feature vectors. The nine kinds of feature vectors are further taken as the inputs of individual support vector machine classifier respectively, and the outputs of these classifiers are aggregated with decision templates. The overall success rate obtained by jackknife cross-validation was 90.95%, indicating our method is very promising for predicting PPI types, might become a useful vehicle for studying the network biology in the post-genomic era.