title: Probabilistic prediction of protein-protein interactions from the protein sequences.
abstract: Prediction of protein-protein interactions is very important for several bioinformatics tasks though it is not a straightforward problem. In this paper, employing only protein sequence information, a framework is presented to predict protein-protein interactions using a probabilistic-based tree augmented nai ve (TAN) Bayesian network. Our framework also provides a confidence level for every predicted interaction, which is useful for further analysis by the biologists. The framework is applied to the yeast interaction datasets for predicting interactions and it is shown that our framework gives better performance than support vector machine (SVM). The framework is implemented as a webserver and is available for prediction.
authors.affiliation: Department of Computer Science, National University of Singapore, Singapore.
authors.forename: Arunkumar
authors.initials: A
authors.lastname: Chinnasamy
authors.forename: Ankush
authors.initials: A
authors.lastname: Mittal
authors.forename: Wing-Kin
authors.initials: WK
authors.lastname: Sung
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D005103:Expert Systems; D005656:Fungal Proteins; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping; D040901:Proteomics; D020539:Sequence Analysis, Protein; D012984:Software; D015003:Yeasts
chemical_list: D005656:Fungal Proteins