Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis
International Journal of Computer Applications in Technology
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Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well.