Abstract
BackgroundProtein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs.ResultsWe present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance.ConclusionsWhen performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.
Highlights
Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms
We report a new sequence-based method for the prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machine (ELM) and Principal component analysis (PCA) aiming at improving the efficiency and effectiveness of the classification accuracy
The PPI dataset was collected from Saccharomyces cerevisiae core subset of database of interacting proteins (DIP), version DIP 20070219
Summary
Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Because the conjoint triad method cannot takes neighboring effect into account and the interactions usually occur in the discontinuous amino acids segments in the sequence, on the other work Guo et al developed a method based on SVM and auto covariance to extract the interactions information in the discontinuous amino acids segments in the sequence [9]. Their method yielded a prediction accuracy of 86.55%, when applied to predicting saccharomyces cerevisiae PPIs. In our previous works, we obtained good prediction performance by using autocorrelation descriptors and correlation coefficient, respectively [8,17]
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