Predicting protein secondary structure using artificial neural networks and information theory

Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in de...

Full description

Saved in:

Bibliographic Details
Main Author:	Abdalla, Saad Osman
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/4309/1/SaadOsmanAbdallaPFSKSM2005.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.4309
record_format	uketd_dc
spelling	my-utm-ep.43092018-01-17T00:29:48Z Predicting protein secondary structure using artificial neural networks and information theory 2005-05 Abdalla, Saad Osman QA Mathematics Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in determining the 3D structure of a protein. In this thesis a method for prediction of protein secondary structure has been proposed and implemented together with other known accurate methods in this domain. The method has been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. A benchmark data set is exploited in training and testing the methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural network to classify a novel protein sequence in one of its three secondary structures classes. Nn-gorv-i is developed and implemented to predict proteins secondary structure using the biological information conserved in neighboring residues and related sequences. The method is further improved by a filtering mechanism for the searched sequences to its advanced version NN-GOR-V-II. The newly developed method is rigorously tested together with the other methods and observed reaches the above 80% level of accuracy. The accuracy and quality of prediction of the newly developed method is superior to all the six methods developed or examined in this research work or that reported in this domain. The Mathews Correlation Coefficients (MCC) proved that NN-GOR-V-IIsecondary structure predicted states are highly related to the observed secondary structure states. The NN-GOR-V-II method is further tested using five DSSP reduction schemes and found stable and reliable in its prediction ability. An additional blind test of sequences that have not been used in the training and testing procedures is conducted and the experimental results show that the NN-GOR-V-II prediction is of high accuracy, quality, and stability. The receiver operating characteristic (ROC) curve and the area under curve (AUC) are applied as novel procedures to assess a multi-class classifier with approximately 0.5 probability of one and only one class. The results of ROC and AUC prove that the NN-GOR-V-II successfully discriminates between two classes; coils and not-coils. 2005-05 Thesis http://eprints.utm.my/id/eprint/4309/ http://eprints.utm.my/id/eprint/4309/1/SaadOsmanAbdallaPFSKSM2005.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Abdalla, Saad Osman Predicting protein secondary structure using artificial neural networks and information theory
description	Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in determining the 3D structure of a protein. In this thesis a method for prediction of protein secondary structure has been proposed and implemented together with other known accurate methods in this domain. The method has been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. A benchmark data set is exploited in training and testing the methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural network to classify a novel protein sequence in one of its three secondary structures classes. Nn-gorv-i is developed and implemented to predict proteins secondary structure using the biological information conserved in neighboring residues and related sequences. The method is further improved by a filtering mechanism for the searched sequences to its advanced version NN-GOR-V-II. The newly developed method is rigorously tested together with the other methods and observed reaches the above 80% level of accuracy. The accuracy and quality of prediction of the newly developed method is superior to all the six methods developed or examined in this research work or that reported in this domain. The Mathews Correlation Coefficients (MCC) proved that NN-GOR-V-IIsecondary structure predicted states are highly related to the observed secondary structure states. The NN-GOR-V-II method is further tested using five DSSP reduction schemes and found stable and reliable in its prediction ability. An additional blind test of sequences that have not been used in the training and testing procedures is conducted and the experimental results show that the NN-GOR-V-II prediction is of high accuracy, quality, and stability. The receiver operating characteristic (ROC) curve and the area under curve (AUC) are applied as novel procedures to assess a multi-class classifier with approximately 0.5 probability of one and only one class. The results of ROC and AUC prove that the NN-GOR-V-II successfully discriminates between two classes; coils and not-coils.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abdalla, Saad Osman
author_facet	Abdalla, Saad Osman
author_sort	Abdalla, Saad Osman
title	Predicting protein secondary structure using artificial neural networks and information theory
title_short	Predicting protein secondary structure using artificial neural networks and information theory
title_full	Predicting protein secondary structure using artificial neural networks and information theory
title_fullStr	Predicting protein secondary structure using artificial neural networks and information theory
title_full_unstemmed	Predicting protein secondary structure using artificial neural networks and information theory
title_sort	predicting protein secondary structure using artificial neural networks and information theory
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2005
url	http://eprints.utm.my/id/eprint/4309/1/SaadOsmanAbdallaPFSKSM2005.pdf
_version_	1747814512570925056

Predicting protein secondary structure using artificial neural networks and information theory

Similar Items