By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Amino Acids. According to news reporting from Guangdong, People's Republic of China, by VerticalNews journalists, research stated, "Protein-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."
The news correspondents obtained a quote from the research from Shenzhen University, "Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. We 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. When 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."
According to the news reporters, the research concluded: "Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time."
For more information on this research see: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. Bmc Bioinformatics, 2013;14 Suppl 8():S10. (BioMed Central - www.biomedcentral.com/; Bmc Bioinformatics - www.biomedcentral.com/bmcbioinformatics/)
Our news journalists report that additional information may be obtained by contacting Z.H. You, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, People's Taiwan. Additional authors for this research include Y.K. Lei, L. Zhu, J. Xia and B. Wang.
Keywords for this news article include: Asia, Peptides, Proteins, Guangdong, Amino Acids, People's Republic of China.
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