title: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.
abstract: Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.
authors.affiliation: Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran. Electronic address: mrbakhtiari@ut.ac.ir.
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Bakhtiarizadeh
authors.affiliation: Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Moradi-Shahrbabak
authors.affiliation: Department of Biology, School of Basic Sciences, University of Qom, Qom, Iran.
authors.forename: Mansour
authors.initials: M
authors.lastname: Ebrahimi
authors.affiliation: Department of Crop Production & Plant Breeding, College of Agriculture, Shiraz University, Shiraz, Iran; School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia. Electronic address: esmaeil.ebrahimie@adelaide.edu.au.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
mesh_terms: D000465:Algorithms; D000818:Animals; D002352:Carrier Proteins / Q000737:chemistry / Q000145:classification / Q000235:genetics; D006801:Humans; D008055:Lipids; D008957:Models, Genetic; D016571:Neural Networks, Computer; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D002352:Carrier Proteins; D008055:Lipids
keywords: Lipid metabolism; Machine learning; Protein features; Support vector machine