Artificial Intelligence System & Protein interaction - Couverture souple

Kumar, Gautam; Lahiri, Tapobrata

 
9783659625336: Artificial Intelligence System & Protein interaction
Couverture souple
ISBN 10 : 3659625337 ISBN 13 : 9783659625336
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

Protein – protein interaction play a major part in Bioinformatics and Computational Biology to interpret the basic inherent principles of biological organizations. Compared to the available protein networks and sequences of dissimilar species, the number of exposed PPIs (protein–protein interactions) is still very much confined. In this book, a sequence-based method using the physio-chemical properties of protein residues is proposed by blending a new feature representation Auto-Covariance (AC) and Artificial Neural Network (ANN). Auto-Covariance describes for interactions among amino acids residues at some outstrip apart in the primary protein sequence, so this method takes the adjacent effect into account for protein interactions. The amino acids were changed into numerical values presenting six physico-chemical properties, after that those numerical sequences were changed to one dimensional vector of same size by AC. Finally, the ANN model was build using the vectors of Auto-Covariance variables as input. The experiment demonstrated that the prediction model and the interaction prediction accuracy is higher than 94.0% after 5 fold cross validation.

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Présentation de l'éditeur

Protein – protein interaction play a major part in Bioinformatics and Computational Biology to interpret the basic inherent principles of biological organizations. Compared to the available protein networks and sequences of dissimilar species, the number of exposed PPIs (protein–protein interactions) is still very much confined. In this book, a sequence-based method using the physio-chemical properties of protein residues is proposed by blending a new feature representation Auto-Covariance (AC) and Artificial Neural Network (ANN). Auto-Covariance describes for interactions among amino acids residues at some outstrip apart in the primary protein sequence, so this method takes the adjacent effect into account for protein interactions. The amino acids were changed into numerical values presenting six physico-chemical properties, after that those numerical sequences were changed to one dimensional vector of same size by AC. Finally, the ANN model was build using the vectors of Auto-Covariance variables as input. The experiment demonstrated that the prediction model and the interaction prediction accuracy is higher than 94.0% after 5 fold cross validation.

Biographie de l'auteur

Er. Gautam is one of the researchers and learner who have been working for protein interaction using artificial intelligence system with such a great accuracy and efficiency of prediction. He is the part of many international Journal Like Journal of Experimental Biology and Agricultural Sciences.Currently he is working on cancer using ANN.

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
2014
Langue
anglais
ISBN 10 
3659625337
ISBN 13 
9783659625336
Reliure
Broché
Nombre de pages
76
Coordonnées du fabricant
non disponible
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