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Issue No. 02 - March-April (2013 vol. 10)
ISSN: 1545-5963
pp: 514-521
Wei Liu , Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China
Dong Li , Beijing Proteome Res. Center, Beijing Inst. of Radiat. Med., Beijing, China
Yunping Zhu , Beijing Proteome Res. Center, Beijing Inst. of Radiat. Med., Beijing, China
Hongwei Xie , Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China
Fuchu He , Beijing Proteome Res. Center, Beijing Inst. of Radiat. Med., Beijing, China
ABSTRACT
The directionality of protein interactions is the prerequisite of forming various signaling networks, and the construction of signaling networks is a critical issue in the discovering the mechanism of the life process. In this paper, we proposed a novel method to infer the directionality in protein-protein interaction networks and furthermore construct signaling networks. Based on the functional annotations of proteins, we proposed a novel parameter GODS and established the prediction model. This method shows high sensitivity and specificity to predict the directionality of protein interactions, evaluated by fivefold cross validation. By taking the threshold value of GODS as 2, we achieved accuracy 95.56 percent and coverage 74.69 percent in the human test set. Also, this method was successfully applied to reconstruct the classical signaling pathways in human. This study not only provided an effective method to unravel the unknown signaling pathways, but also the deeper understanding for the signaling networks, from the aspect of protein function.
INDEX TERMS
Proteins, Accuracy, Training, Humans, Protein engineering, Bioinformatics, Predictive models,gene ontology, Proteins, Accuracy, Training, Humans, Protein engineering, Bioinformatics, Predictive models, cross validation, Signaling network, protein interaction
CITATION
Wei Liu, Dong Li, Yunping Zhu, Hongwei Xie, Fuchu He, "Reconstruction of Signaling Network from Protein Interactions Based on Function Annotations", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 514-521, March-April 2013, doi:10.1109/TCBB.2013.20
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