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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
http://eprints.utm.my/id/eprint/11488/
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spelling my.utm.114882017-09-18T10:11:02Z http://eprints.utm.my/id/eprint/11488/ Two-layer SVM classifier for remote protein homology detection and fold recognition 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/11488/6/MohdHilmiMudaMFSKSM2009.pdf Muda, Mohd. Hilmi (2009) Two-layer SVM classifier for remote protein homology detection and fold recognition. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2009
url http://eprints.utm.my/id/eprint/11488/6/MohdHilmiMudaMFSKSM2009.pdf
http://eprints.utm.my/id/eprint/11488/
_version_ 1643645695970770944
score 13.211869

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