Synopsis
Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. Most proteins function in collaboration with other proteins and one goal of proteomics is to identify which proteins interact. This is especially useful in determining potential partners in cell signaling cascades. A number of techniques have been developed for the identification and classification of protein-protein interactions. The techniques developed in past years are still far from perfect. The Jordan neural network classification model tries to overcome this problem. The Jordan Neural Network takes amino acid composition of protein pairs to classify them interacting and non-interacting. Jordan neural network classification model outperforms the other methods for protein-protein interaction classification. Jordan neural network classification model proves to be better model with higher accuracy along with improved specificity and sensitivity than the various existing techniques.
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Protein-Protein Interactions Classification: Based on Recurrent Neural Network
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ISBN 10: 365926086X
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Protein-Protein Interactions Classification
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. Most proteins function in collaboration with other proteins and one goal of proteomics is to identify which proteins interact. This is especially useful in determining potential partners in cell signaling cascades. A number of techniques have been developed for the identification and classification of protein-protein interactions. The techniques developed in past years are still far from perfect. The Jordan neural network classification model tries to overcome this problem. The Jordan Neural Network takes amino acid composition of protein pairs to classify them interacting and non-interacting. Jordan neural network classification model outperforms the other methods for protein-protein interaction classification. Jordan neural network classification model proves to be better model with higher accuracy along with improved specificity and sensitivity than the various existing techniques. 88 pp. Englisch. Seller Inventory # 9783659260865
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Protein-Protein Interactions Classification
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. Most proteins function in collabora. Seller Inventory # 5143878
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Protein-Protein Interactions Classification: Based On Recurrent Neural Network
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Protein-Protein Interactions Classification
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Taschenbuch. Condition: Neu. Neuware -Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. Most proteins function in collaboration with other proteins and one goal of proteomics is to identify which proteins interact. This is especially useful in determining potential partners in cell signaling cascades. A number of techniques have been developed for the identification and classification of protein-protein interactions. The techniques developed in past years are still far from perfect. The Jordan neural network classification model tries to overcome this problem. The Jordan Neural Network takes amino acid composition of protein pairs to classify them interacting and non-interacting. Jordan neural network classification model outperforms the other methods for protein-protein interaction classification. Jordan neural network classification model proves to be better model with higher accuracy along with improved specificity and sensitivity than the various existing techniques.Books on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch. Seller Inventory # 9783659260865
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Protein-Protein Interactions Classification : Based on Recurrent Neural Network
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. Most proteins function in collaboration with other proteins and one goal of proteomics is to identify which proteins interact. This is especially useful in determining potential partners in cell signaling cascades. A number of techniques have been developed for the identification and classification of protein-protein interactions. The techniques developed in past years are still far from perfect. The Jordan neural network classification model tries to overcome this problem. The Jordan Neural Network takes amino acid composition of protein pairs to classify them interacting and non-interacting. Jordan neural network classification model outperforms the other methods for protein-protein interaction classification. Jordan neural network classification model proves to be better model with higher accuracy along with improved specificity and sensitivity than the various existing techniques. Seller Inventory # 9783659260865
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Protein-Protein Interactions Classification | Based on Recurrent Neural Network
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Taschenbuch. Condition: Neu. Protein-Protein Interactions Classification | Based on Recurrent Neural Network | Dilpreet Kaur (u. a.) | Taschenbuch | 88 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659260865 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106210637