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Resources » Paper

Guo Y et al. (2010) BMC Res Notes "PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment."

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  • Comments on Guo Y et al. (2010) BMC Res Notes "PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment." (0)

  • Overview

    PMID:
    20500905
    DOI:
    10.1186/1756-0500-3-145
    Status:
    Publication type:
    Journal_article
    WormBase ID:
    WBPaper00036307

    Guo Y, Li M, Pu X, Li G, Guang X, Xiong W, & Li J (2010). PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment. BMC Res Notes, 3, 145. doi:10.1186/1756-0500-3-145

    UNLABELLED: ABSTRACT: BACKGROUND: Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance. FINDINGS: Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, Drosophila, Escherichia coli, and Caenorhabditis elegans. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for Drosophila, 92.73% for E. coli, and 97.51% for C. elegans. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of >/=0.8, indicating that this tool could predict novel PPIs with high confidence. CONCLUSIONS: Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html.


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