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Prediction of regulation relationship between protein interactions in signaling networks.

Abstract：

:The discovery of regulation relationship of protein interactions is crucial for the mechanism research in signaling network. Bioinformatics methods can be used to accelerate the discovery of regulation relationship between protein interactions, to distinguish the activation relations from inhibition relations. In this paper, we describe a novel method to predict the regulation relations of protein interactions in the signaling network. We detected 4,417 domain pairs that were significantly enriched in the activation or inhibition dataset. Three machine learning methods, logistic regression, support vector machines(SVMs), and naïve bayes, were explored in the classifier models. The prediction power of three different models was evaluated by 5-fold cross-validation and the independent test dataset. The area under the receiver operating characteristic curve for logistic regression, SVM, and naïve bayes models was 0.946, 0.905 and 0.809, respectively. Finally, the logistic regression classifier was applied to the human proteome-wide interaction dataset, and 2,591 interactions were predicted with their regulation relations, with 2,048 in activation and 543 in inhibition. This model based on domains can be used to identify the regulation relations between protein interactions and furthermore reconstruct signaling pathways.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Liu W,Xie H

doi

10.1016/j.bbrc.2013.09.093

subject

Has Abstract

pub_date

2013-10-25 00:00:00

pages

388-92

issue

3

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(13)01586-6

journal_volume

440

pub_type

杂志文章

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