Layer svm classifier remote de hilmi muda (5 resultados)

Taschenbuch. Condición: Neu. 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 128 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hilmi Muda MohdMohd 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 .…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…