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Protein Secondary Structure Prediction: Using Artificial Neural Networks and Information Theory - Softcover
Synopsis
Large genome sequencing projects generate huge number of protein sequences in their primary structures that are difficult for conventional biological techniques to determine their corresponding 3D structures and hence their functions. In this book, a novel method for prediction of protein secondary structure has been proposed and implemented together with other known methods in this domain. A benchmark data set is used in training and testing the methods under the same hardware, platforms, and environments. The methods in this work have been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. In this book, the developed method utilizes the knowledge of the information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes using the biological information conserved in neighboring residues and related sequences. The accuracy and quality of prediction of the newly developed method found superior to all other methods reported in this domain. In this book, a clear methodology and stringent statistical analysis and interpretation are presented.
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About the Author
Dr. Saad Subair was born on the bank of the river Nile 40 kms away from the capital Khartoum. He graduated with BSc from U of K, PGD,MSc and PhD in Bioinformatics from UTM Malaysia, and MSc in Genetics from UPM. Dr Subair is a contributing author to a book published by IGI pub.USA. He is Associate Prof in Al-Zaim Al-Azhari University, Khartoum.
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Protein Secondary Structure Prediction
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LAP LAMBERT Academic Publishing Dez 2011, 2011
ISBN 10: 3847330667
ISBN 13: 9783847330660
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Large genome sequencing projects generate huge number of protein sequences in their primary structures that are difficult for conventional biological techniques to determine their corresponding 3D structures and hence their functions. In this book, a novel method for prediction of protein secondary structure has been proposed and implemented together with other known methods in this domain. A benchmark data set is used in training and testing the methods under the same hardware, platforms, and environments. The methods in this work have been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. In this book, the developed method utilizes the knowledge of the information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes using the biological information conserved in neighboring residues and related sequences. The accuracy and quality of prediction of the newly developed method found superior to all other methods reported in this domain. In this book, a clear methodology and stringent statistical analysis and interpretation are presented. 272 pp. Englisch. Seller Inventory # 9783847330660
Quantity: 2 available
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Protein Secondary Structure Prediction
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3847330667
ISBN 13: 9783847330660
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Subair SaadDr. Saad Subair was born on the bank of the river Nile 40 kms away from the capital Khartoum. He graduated with BSc from U of K, PGD,MSc and PhD in Bioinformatics from UTM Malaysia, and MSc in Genetics from UPM. Dr Subair. Seller Inventory # 5510552
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Protein Secondary Structure Prediction
Published by
LAP LAMBERT Academic Publishing Dez 2011, 2011
ISBN 10: 3847330667
ISBN 13: 9783847330660
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Large genome sequencing projects generate huge number of protein sequences in their primary structures that are difficult for conventional biological techniques to determine their corresponding 3D structures and hence their functions. In this book, a novel method for prediction of protein secondary structure has been proposed and implemented together with other known methods in this domain. A benchmark data set is used in training and testing the methods under the same hardware, platforms, and environments. The methods in this work have been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. In this book, the developed method utilizes the knowledge of the information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes using the biological information conserved in neighboring residues and related sequences. The accuracy and quality of prediction of the newly developed method found superior to all other methods reported in this domain. In this book, a clear methodology and stringent statistical analysis and interpretation are presented.Books on Demand GmbH, Überseering 33, 22297 Hamburg 272 pp. Englisch. Seller Inventory # 9783847330660
Quantity: 2 available
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Protein Secondary Structure Prediction : Using Artificial Neural Networks and Information Theory
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3847330667
ISBN 13: 9783847330660
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Large genome sequencing projects generate huge number of protein sequences in their primary structures that are difficult for conventional biological techniques to determine their corresponding 3D structures and hence their functions. In this book, a novel method for prediction of protein secondary structure has been proposed and implemented together with other known methods in this domain. A benchmark data set is used in training and testing the methods under the same hardware, platforms, and environments. The methods in this work have been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. In this book, the developed method utilizes the knowledge of the information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes using the biological information conserved in neighboring residues and related sequences. The accuracy and quality of prediction of the newly developed method found superior to all other methods reported in this domain. In this book, a clear methodology and stringent statistical analysis and interpretation are presented. Seller Inventory # 9783847330660
Quantity: 1 available