title: LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information.
abstract: Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse.
  
  
  CONCLUSIONS
  The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems.
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
authors.forename: Javad
authors.initials: J
authors.lastname: Zahiri
authors.affiliation: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
authors.forename: Morteza
authors.initials: M
authors.lastname: Mohammad-Noori
authors.affiliation: Brain and Intelligent Systems Research Lab, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Ebrahimpour
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Samaneh
authors.initials: S
authors.lastname: Saadat
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Joseph H
authors.initials: JH
authors.lastname: Bozorgmehr
authors.affiliation: Department for Bioinformatics and Computational Biology, Faculty of Informatics, TUM, Garching 85748, Germany.
authors.forename: Tatyana
authors.initials: T
authors.lastname: Goldberg
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. Electronic address: amasoudin@ibb.ut.ac.ir.
authors.forename: Ali
authors.initials: A
authors.lastname: Masoudi-Nejad
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D055432:Metabolomics / Q000379:methods*; D011485:Protein Binding; D011499:Protein Processing, Post-Translational; D021381:Protein Transport; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D020543:Proteome
keywords: Cellular localization; Classifier fusion; Ensemble learning; Machine learning; Protein–protein interaction