These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: RBPPred: predicting RNA-binding proteins from sequence using SVM.
    Author: Zhang X, Liu S.
    Journal: Bioinformatics; 2017 Mar 15; 33(6):854-862. PubMed ID: 27993780.
    Abstract:
    MOTIVATION: Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design. RESULTS: In this study, we present the RBPPred (an RNA-binding protein predictor), a new method based on the support vector machine, to predict whether a protein binds RNAs, based on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, the new approach RBPPred performed much better than state-of-the-art methods. The results show that RBPPred correctly predicted 83% of 2780 RBPs and 96% out of 7093 non-RBPs with MCC of 0.808 using the 10-fold cross validation. Furthermore, we achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. In addition we tested the capability of RBPPred to identify new RBPs, which further confirmed the practicability and predictability of the method. AVAILABILITY AND IMPLEMENTATION: RBPPred program can be accessed at: http://rnabinding.com/RBPPred.html . CONTACT: liushiyong@gmail.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    [Abstract] [Full Text] [Related] [New Search]