ISBN: 9783846582688 - an in-silico approach for protein secondary structure modeling: prediction of helical transmembrane region using artificial neural network approach through matlab (9 resultados)

Condición: New. pp. 56.

Taschenbuch. Condición: Neu. An In-silico Approach For Protein Secondary Structure Modeling | Prediction of helical transmembrane region using Artificial Neural Network Approach through MATLAB | Sugandha Sharma (u. a.) | Taschenbuch | 56 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783846582688 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The structural knowledge of transmembrane proteins is difficult to attain experimentally, as the wet-lab experimental methods are time consuming, expensive, require good infrastructure and contain high false positive results. Hence, the need of in-silico methods for protein secondary structure prediction is being driven by above listed limitations. Over a number of years, various transmembrane region predictors has been developed using computational approach. In this book, a connectionist (ANN-Artificial Neural Network) model has been developed for prediction of alpha helical transmembrane region using amino acid properties rather than using the traditional hydrophobicity approach. The best connectionist model developed in this book, achieved an accuracy of 73.31%, which seems to be relatively better than the methods developed earlier by different researchers. This book will be equally helpful to bioinformatician, molecular biologist as well as computational biologist in solving various complex biological problems that could not be solved with the traditional approach. 56 pp. Englisch.…

Condición: New. Print on Demand pp. 56 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The structural knowledge of transmembrane proteins is difficult to attain experimentally, as the wet-lab experimental methods are time consuming, expensive, require good infrastructure and contain high false positive results. Hence, the need of in-silico methods for protein secondary structure prediction is being driven by above listed limitations. Over a number of years, various transmembrane region predictors has been developed using computational approach. In this book, a connectionist (ANN-Artificial Neural Network) model has been developed for prediction of alpha helical transmembrane region using amino acid properties rather than using the traditional hydrophobicity approach. The best connectionist model developed in this book, achieved an accuracy of 73.31%, which seems to be relatively better than the methods developed earlier by different researchers. This book will be equally helpful to bioinformatician, molecular biologist as well as computational biologist in solving various complex biological problems that could not be solved with the traditional approach.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma SugandhaMs. Sugandha Sharma is a Research Scholar in Bioinformatics. Her research interests are Protein-Protein interactions, Machine learning techniques, Computational & Systems Biology. The co-author Dr. A.K. Sharma is Sr. S. …

Condición: New. PRINT ON DEMAND pp. 56.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The structural knowledge of transmembrane proteins is difficult to attain experimentally, as the wet-lab experimental methods are time consuming, expensive, require good infrastructure and contain high false positive results. Hence, the need of in-silico methods for protein secondary structure prediction is being driven by above listed limitations. Over a number of years, various transmembrane region predictors has been developed using computational approach. In this book, a connectionist (ANN-Artificial Neural Network) model has been developed for prediction of alpha helical transmembrane region using amino acid properties rather than using the traditional hydrophobicity approach. The best connectionist model developed in this book, achieved an accuracy of 73.31%, which seems to be relatively better than the methods developed earlier by different researchers. This book will be equally helpful to bioinformatician, molecular biologist as well as computational biologist in solving various complex biological problems that could not be solved with the traditional approach.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.…