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A Prediction Model for Membrane Proteins Using Moments Based Features
Joint Authors
Khan, Sher Afzal
Khan, Yaser Daanial
Butt, Ahmad Hassan
Jamil, Hamza
Rasool, Nouman
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-15
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Butt, Ahmad Hassan& Khan, Sher Afzal& Jamil, Hamza& Rasool, Nouman& Khan, Yaser Daanial. 2016. A Prediction Model for Membrane Proteins Using Moments Based Features. BioMed Research International،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099040
Modern Language Association (MLA)
Butt, Ahmad Hassan…[et al.]. A Prediction Model for Membrane Proteins Using Moments Based Features. BioMed Research International No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1099040
American Medical Association (AMA)
Butt, Ahmad Hassan& Khan, Sher Afzal& Jamil, Hamza& Rasool, Nouman& Khan, Yaser Daanial. A Prediction Model for Membrane Proteins Using Moments Based Features. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099040
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099040