title: Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks.
abstract: In this study, membrane proteins were classified using the information hidden in their sequences. It was achieved by applying the wavelet analysis to the sequences and consequently extracting several features, each of them revealing a proportion of the information content present in the sequence. The resultant features were made normalized and subsequently fed into a cascaded model developed in order to reduce the effect of the existing bias in the dataset, rising from the difference in size of the membrane protein classes. The results indicate an improvement in prediction accuracy of the model in comparison with similar works. The application of the presented model can be extended to other fields of structural biology due to its efficiency, simplicity and flexibility.
authors.affiliation: Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box 14115-175, Gisha, Tehran, Iran. ma_rezaei@modares.ac.ir
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Rezaei
authors.forename: Parviz
authors.initials: P
authors.lastname: Abdolmaleki
authors.forename: Zahra
authors.initials: Z
authors.lastname: Karami
authors.forename: Ebrahim Barzegari
authors.initials: EB
authors.lastname: Asadabadi
authors.forename: Mohammad Amin
authors.initials: MA
authors.lastname: Sherafat
authors.forename: Hamid
authors.initials: H
authors.lastname: Abrishami-Moghaddam
authors.forename: Marziyeh
authors.initials: M
authors.lastname: Fadaie
authors.forename: Mohammad
authors.initials: M
authors.lastname: Forouzanfar
mesh_terms: D000465:Algorithms; D000818:Animals; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry / Q000145:classification*; D008956:Models, Chemical; D016571:Neural Networks, Computer
chemical_list: D008565:Membrane Proteins