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Title: Probabilistic prediction of protein-protein interactions from the protein sequences
Authors: Chinnasamy, A.
Mittal, A.
Sung, W.-K. 
Keywords: Machine learning
Protein feature extraction
Protein-protein interaction
TAN Bayesian classifier
Issue Date: 2006
Citation: Chinnasamy, A., Mittal, A., Sung, W.-K. (2006). Probabilistic prediction of protein-protein interactions from the protein sequences. Computers in Biology and Medicine 36 (10) : 1143-1154. ScholarBank@NUS Repository.
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{dotless}̈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. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Computers in Biology and Medicine
ISSN: 00104825
DOI: 10.1016/j.compbiomed.2005.09.005
Appears in Collections:Staff Publications

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