Clustering and Classification Methods Used in Biosequence Analysis: New Approaches - Couverture souple

Kandemir-Cavas, Cagin; Nasibov, Efendi

 
9783846551837: Clustering and Classification Methods Used in Biosequence Analysis: New Approaches
Couverture souple
ISBN 10 : 384655183X ISBN 13 : 9783846551837
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

Since human genome studies have brought out a huge number of biosequence data, computational techniques have been developed preventing the vast of cost and time in the management process of these data. In this book, new approaches on clustering and classification methods in biosequence –protein, enzyme sequences– analysis are studied. Classification is a supervised learning algorithm that aims at categorizing or assigning class labels to a pattern set under the supervision of an expert. Therefore, the prediction of subcellular location of proteins and the classification of enzymes have been solved via data mining techniques. Clustering is an unsupervised learning technique that aims at decomposing a given set of elements into clusters based on similarity. Due to the fact that protein sequences have evolutionary relationship, all protein sequences can be organized in terms of their sequence similarity. A graphical illustration called phylogenetic tree can summarize the relationship between the protein sequences. The construction of phylogenetic tree is based on hierarchical clustering. Thus, we have proposed a new method as a linkage method in construction phylogenetic tree.

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

Présentation de l'éditeur

Since human genome studies have brought out a huge number of biosequence data, computational techniques have been developed preventing the vast of cost and time in the management process of these data. In this book, new approaches on clustering and classification methods in biosequence –protein, enzyme sequences– analysis are studied. Classification is a supervised learning algorithm that aims at categorizing or assigning class labels to a pattern set under the supervision of an expert. Therefore, the prediction of subcellular location of proteins and the classification of enzymes have been solved via data mining techniques. Clustering is an unsupervised learning technique that aims at decomposing a given set of elements into clusters based on similarity. Due to the fact that protein sequences have evolutionary relationship, all protein sequences can be organized in terms of their sequence similarity. A graphical illustration called phylogenetic tree can summarize the relationship between the protein sequences. The construction of phylogenetic tree is based on hierarchical clustering. Thus, we have proposed a new method as a linkage method in construction phylogenetic tree.

Biographie de l'auteur

Dr. Kandemir-Cavas has obtained her PhD degree in Statistics in 2010. She is currently an Assistant Professor at the Department of Computer Science at Dokuz Eylül University in Izmir, Turkey.

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 
384655183X
ISBN 13 
9783846551837
Reliure
Broché
Nombre de pages
104
Coordonnées du fabricant
non disponible
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