Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex

Herman, Nanna Suryana and Husin, Nurul Arneida and Hussin, Burairah (2012) Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex. International Journal Bioautomation, 15. pp. 251-260. ISSN 1549-3636

Text Comparative_Analysis_using_Bayesian_Approach_to_Ne.pdf - Published Version Download (364kB)

Abstract

Widely accepted as an important signal for gene discovery,translation initiation sites (TIS) in weak context has been the main focus in this paper.Many TIS prediction programs have been developed for optimal context,but they fail to successfully predict the start codon if the contexts conditions are in weak positions. The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%.

Item Type:	Article
Uncontrolled Keywords:	Bayesian classifier, Neural network, Translation initiation sites, Weak context, Information Gain Ratio, Classification algorithms.
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Mohd. Nazir Taib
Date Deposited:	08 Jul 2019 02:32
Last Modified:	29 Aug 2021 22:30
URI:	http://eprints.utem.edu.my/id/eprint/22976
Statistic Details:	View Download Statistic

Actions (login required)

View Item