Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis
TLDR
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 is presented.Abstract:
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. 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. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.read more
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References
More filters
Journal ArticleDOI
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
Journal ArticleDOI
Functional organization of the yeast proteome by systematic analysis of protein complexes
Anne-Claude Gavin,Markus Bösche,Roland Krause,Paola Grandi,Martina Marzioch,Andreas Bauer,Jörg Schultz,Jens Rick,Anne-Marie Michon,Cristina-Maria Cruciat,Marita Remor,Christian Höfert,Malgorzata Schelder,Miro Brajenovic,Heinz Ruffner,Alejandro Merino,Karin Klein,Manuela Hudak,David Dickson,Tatjana Rudi,Volker Gnau,Angela Bauch,Sonja Bastuck,Bettina Huhse,Christina Leutwein,Marie-Anne Heurtier,Richard R. Copley,Angela Edelmann,Erich Querfurth,Vladimir Rybin,Gerard Drewes,Manfred Raida,Tewis Bouwmeester,Peer Bork,Bertrand Séraphin,Bernhard Kuster,Gitte Neubauer,Giulio Superti-Furga +37 more
TL;DR: The analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions, which contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.
Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
A comprehensive two-hybrid analysis to explore the yeast protein interactome
TL;DR: The comprehensive analysis using a system to examine two-hybrid interactions in all possible combinations between the budding yeast Saccharomyces cerevisiae is completed and would significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.
Journal ArticleDOI
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Yuen Ho,Albrecht Gruhler,Adrian Heilbut,Gary D. Bader,Gary D. Bader,Lynda Moore,Sally-Lin Adams,Anna Millar,Paul J. Taylor,Keiryn L. Bennett,Kelly Boutilier,Lingyun Yang,Cheryl Wolting,Ian Donaldson,Søren Schandorff,Juanita Shewnarane,Mai Vo,Joanne Taggart,Marilyn Goudreault,Brenda Muskat,Cris Alfarano,Danielle Dewar,Zhen Lin,Katerina Michalickova,Katerina Michalickova,Andrew Willems,Andrew Willems,Holly Sassi,Peter A Nielsen,Karina Juhl Rasmussen,Jens R. Andersen,Lene E. Johansen,Lykke Haastrup Hansen,Hans Jespersen,Alexandre V. Podtelejnikov,Eva Nielsen,Janne S. Crawford,Vibeke Poulsen,Birgitte D Sørensen,Jesper Matthiesen,Ronald C. Hendrickson,Frank Gleeson,Tony Pawson,Tony Pawson,Michael Moran,Daniel Durocher,Daniel Durocher,Matthias Mann,Christopher W. V. Hogue,Christopher W. V. Hogue,Daniel Figeys,Mike Tyers,Mike Tyers +52 more
TL;DR: Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies.
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