Articles liés à 2 Layer SVM Classifier for Remote Protein Homology...

2 Layer SVM Classifier for Remote Protein Homology & Fold Recognition: SVM Classifier for Remote Protein Homology Detection and Fold Recognition - Couverture souple

Hilmi Muda, Mohd

 
9783847321088: 2 Layer SVM Classifier for Remote Protein Homology & Fold Recognition: SVM Classifier for Remote Protein Homology Detection and Fold Recognition
Couverture souple
ISBN 10 : 3847321080 ISBN 13 : 9783847321088
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

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 result shows that the Two-Layer SVM Classifier is capable to perform wellusing different versions of datasets and has outperformed existing methods, which implies the reliability of the framework.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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 result shows that the Two-Layer SVM Classifier is capable to perform wellusing different versions of datasets and has outperformed existing methods, which implies the reliability of the framework.

Biographie de l'auteur

Mohd Hilmi Muda was born in Kuala Terengganu, Malaysia. He graduated in Master in Science Computer within 2 semesters. He also holds Degree in Science Computer and Diploma in Science Computer from Universiti Teknologi Malaysia, Johor.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3847321080
ISBN 13 
9783847321088
Reliure
Broché
Nombre de pages
128
Coordonnées du fabricant
non disponible
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