2 Layer SVM Classifier for Remote Protein Homology & Fold Recognition | SVM Classifier for Remote Protein Homology Detection and Fold Recognition

Mohd Hilmi Muda

ISBN 10: 3847321080 / ISBN 13: 9783847321088

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

2 Layer SVM Classifier for Remote Protein Homology & Fold Recognition | SVM Classifier for Remote Protein Homology Detection and Fold Recognition | Mohd Hilmi Muda | Taschenbuch | 128 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783847321088 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 106676697

Bibliographic Details

• Title: 2 Layer SVM Classifier for Remote Protein Homology & Fold Recognition | SVM Classifier for Remote Protein Homology Detection and Fold Recognition
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3847321080
• ISBN 13: 9783847321088
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 209 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

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.