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.
"Sinopsis" puede pertenecer a otra edición de este libro.
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.
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 23,00 gastos de envío desde Alemania a Estados Unidos de America
Destinos, gastos y plazos de envíoLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783847330660
Cantidad disponible: 2 disponibles
Librería: moluna, Greven, Alemania
Condición: 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. Nº de ref. del artículo: 5510552
Cantidad disponible: Más de 20 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783847330660
Cantidad disponible: 2 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783847330660
Cantidad disponible: 1 disponibles