title: A Prediction Model for Membrane Proteins Using Moments Based Features.
abstract: The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.
authors.affiliation: Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
authors.forename: Ahmad Hassan
authors.initials: AH
authors.lastname: Butt
authors.affiliation: Faculty of Computing and Information Technology in Rabigh, King Abdul Aziz University, Saudi Arabia.
authors.forename: Sher Afzal
authors.initials: SA
authors.lastname: Khan
authors.affiliation: Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
authors.forename: Hamza
authors.initials: H
authors.lastname: Jamil
authors.affiliation: Department of Chemistry, School of Science, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
authors.forename: Nouman
authors.initials: N
authors.lastname: Rasool
authors.affiliation: Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
authors.forename: Yaser Daanial
authors.initials: YD
authors.lastname: Khan
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D002462:Cell Membrane / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D008051:Lipid Bilayers / Q000737:chemistry / Q000378:metabolism; D008565:Membrane Proteins / Q000737:chemistry* / Q000378:metabolism; D008962:Models, Theoretical; D016571:Neural Networks, Computer
chemical_list: D000596:Amino Acids; D008051:Lipid Bilayers; D008565:Membrane Proteins