Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks.
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.
Rezaei
Mohammad Ali
MA
Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box 14115-175, Gisha, Tehran, Iran. ma_rezaei@modares.ac.ir
Abdolmaleki
Parviz
P
Karami
Zahra
Z
Asadabadi
Ebrahim Barzegari
EB
Sherafat
Mohammad Amin
MA
Abrishami-Moghaddam
Hamid
H
Fadaie
Marziyeh
M
Forouzanfar
Mohammad
M
D000465:Algorithms; D000818:Animals; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry / Q000145:classification*; D008956:Models, Chemical; D016571:Neural Networks, Computer
D008565:Membrane Proteins