Two-layer SVM classifier for remote protein homology detection and fold recognition

Advances in molecular biology in the past years have yielded an unprecedented amount of new protein sequences. The resulting sequences describe a protein in terms of the amino acids that constitute them without structural or functional protein information. Therefore, remote protein homology detectio...

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Main Author:	Muda, Mohd. Hilmi
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11488/6/MohdHilmiMudaMFSKSM2009.pdf
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id	my-utm-ep.11488
record_format	uketd_dc
spelling	my-utm-ep.114882017-09-18T10:11:02Z Two-layer SVM classifier for remote protein homology detection and fold recognition 2009-11 Muda, Mohd. Hilmi QA75 Electronic computers. Computer science Advances in molecular biology in the past years have yielded an unprecedented amount of new protein sequences. The resulting sequences describe a protein in terms of the amino acids that constitute them without structural or functional protein information. Therefore, remote protein homology detection and fold recognition algorithms have become increasingly important to detect the structural homology in proteins where there are small or no similarity at all in the sequences compared. However, it is a challenging task to detect and classify this similarity with more biological meaning in the context of Structural Classification of Proteins (SCOP) database. This study presents a new computational framework based on two-layer SVM classifier that uses protein sequences as a primary source. The first layer is used to detect up to superfamily level in the SCOP hierarchy using one-versus-all SVM binary classifiers and the Bio-kernel function. The second layer uses SVM with fold recognition codes and the profile-string kernel to leverage the unlabeled data and to detect up to fold level in the SCOP hierarchy. The proposed framework is tested using SCOP 1.53, 1.67 and 1.73 datasets and the results are evaluated using mean Receiver Operating Characteristics (ROC) and mean Median Rate of False Positives (MRFP). In terms of mean ROC, the experiment shows 4.19% improvement in SCOP 1.53 dataset, 4.75% in SCOP 1.67 dataset and 4.03% in SCOP 1.73 dataset compared to the existing SVM-based classifiers and kernel functions. This result shows that the proposed framework is capable to perform well using different versions of datasets and has outperformed existing methods, which implies the reliability of the framework. 2009-11 Thesis http://eprints.utm.my/id/eprint/11488/ http://eprints.utm.my/id/eprint/11488/6/MohdHilmiMudaMFSKSM2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Muda, Mohd. Hilmi Two-layer SVM classifier for remote protein homology detection and fold recognition
description	Advances in molecular biology in the past years have yielded an unprecedented amount of new protein sequences. The resulting sequences describe a protein in terms of the amino acids that constitute them without structural or functional protein information. Therefore, remote protein homology detection and fold recognition algorithms have become increasingly important to detect the structural homology in proteins where there are small or no similarity at all in the sequences compared. However, it is a challenging task to detect and classify this similarity with more biological meaning in the context of Structural Classification of Proteins (SCOP) database. This study presents a new computational framework based on two-layer SVM classifier that uses protein sequences as a primary source. The first layer is used to detect up to superfamily level in the SCOP hierarchy using one-versus-all SVM binary classifiers and the Bio-kernel function. The second layer uses SVM with fold recognition codes and the profile-string kernel to leverage the unlabeled data and to detect up to fold level in the SCOP hierarchy. The proposed framework is tested using SCOP 1.53, 1.67 and 1.73 datasets and the results are evaluated using mean Receiver Operating Characteristics (ROC) and mean Median Rate of False Positives (MRFP). In terms of mean ROC, the experiment shows 4.19% improvement in SCOP 1.53 dataset, 4.75% in SCOP 1.67 dataset and 4.03% in SCOP 1.73 dataset compared to the existing SVM-based classifiers and kernel functions. This result shows that the proposed framework is capable to perform well using different versions of datasets and has outperformed existing methods, which implies the reliability of the framework.
format	Thesis
qualification_level	Master's degree
author	Muda, Mohd. Hilmi
author_facet	Muda, Mohd. Hilmi
author_sort	Muda, Mohd. Hilmi
title	Two-layer SVM classifier for remote protein homology detection and fold recognition
title_short	Two-layer SVM classifier for remote protein homology detection and fold recognition
title_full	Two-layer SVM classifier for remote protein homology detection and fold recognition
title_fullStr	Two-layer SVM classifier for remote protein homology detection and fold recognition
title_full_unstemmed	Two-layer SVM classifier for remote protein homology detection and fold recognition
title_sort	two-layer svm classifier for remote protein homology detection and fold recognition
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/11488/6/MohdHilmiMudaMFSKSM2009.pdf
_version_	1747814862574059520

Two-layer SVM classifier for remote protein homology detection and fold recognition

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