Mostafa abdelazim (22 risultati)

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Softcover/Taschenbuch. Condizione: Neu. A previs?o bem-sucedida dos ep?topos das mol?culas do Complexo Principal de Histocompatibilidade (MHC) ? um passo essencial na conce??o de vacinas gen?ticas. A previs?o bem-sucedida dos ep?topos do MHC de classe II ? mais dif?cil do que a dos ep?topos do MHC de classe I, devido ? ranhura de liga??o aberta em ambas as extremidades das mol?culas de classe II; esta estrutura leva a um comprimento vari?vel dos ep?topos do MHC II e complica a tarefa de detetar o 9-mer de liga??o central. Neste livro, apresentamos um novo algoritmo de classifica??o para a previs?o de ep?topos do MHC de classe II utilizando a t?cnica de Aprendizagem de Inst?ncias M?ltiplas. O Conjunto de Agrupamento Construtivo Separado (SCCE) ? a nossa nova vers?o do Conjunto de Agrupamento Construtivo (CCE). O SCCE integrou o Algoritmo Gen?tico, o agrupamento K-medoides, a aprendizagem em conjunto e a m?quina de vetores de suporte numa orquestra??o para prever os ep?topos do MHC II. O SCCE foi testado em quatro conjuntos de dados de refer?ncia e alcan?ou uma precis?o m?dia de 85%. Os resultados do SCCE superam a maioria dos m?todos de regress?o de ponta atuais. O SCCE alcan?ou estes resultados utilizando apenas indicadores de liga??o e n?o liga??o, sem necessidade de dados de regress?o. Gewicht in Gramm: 185.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Nabil EmadEmad Nabil and Amr Badr work in the Department of Computer Science, Faculty of Computers and Information, Cairo University, Egypt. Mostafa Abdelazim works in the College of Computing and Information Technology, Arab Academy.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Elsemellawy HossamHossam Elsemellawy is Researcher in the Arab Academy for Science, Technology and Maritime Transport, Egypt. Amr Badr is a Prof. of Computer Science, Cairo University, Egypt. Mostafa Abdelazim is a Prof. of Computer . …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem-solving techniques. In this book, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. The hybrid algorithm used for evolving a fuzzy rule system to solve the well known Wisconsin Breast Cancer Diagnosis problem (WBCD). The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of the hybrid algorithm was verified through its application to a binary character recognition problem, the hybrid algorithm overcomes also both GAs and AIS and reached the convergence point before them.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Successful Prediction for Major Histocompatibility Complex (MHC) molecule epitopes is an essential step in designing Genetic Vaccines. Successful prediction of MHC class II epitopes is more difficult than MHC class I epitopes due to open binding groove at both ends in class II molecules, this structure leads to variable length for MHC II epitopes and complicating the task for detecting the core binding 9-mer. In this Book we presented a novel classification algorithm for predicting MHC Class II epitopes using Multiple Instance Learning technique. Separated Constructive Clustering Ensemble (SCCE) is our new version for Constructive Clustering Ensemble (CCE). SCCE integrated Genetic Algorithm, K medoid clustering, Ensemble learning and Support vector machine in an orchestration to predict the MHC II epitopes. SCCE is tested by four benchmark data sets and achieved average accuracy 85%. SCCE results exceed most of the current state of art regression methods. SCCE achieved these results using only binder and non-binder flags, without need for regression data.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - La prédiction réussie des épitopes des molécules du complexe majeur d'histocompatibilité (CMH) constitue une étape essentielle dans la conception de vaccins génétiques. La prédiction des épitopes du CMH de classe II s'avère plus difficile que celle des épitopes du CMH de classe I en raison de la rainure de liaison ouverte aux deux extrémités des molécules de classe II ; cette structure entraîne une longueur variable pour les épitopes du CMH de classe II et complique la détection du 9-mère de liaison central. Dans cet ouvrage, nous présentons un nouvel algorithme de classification permettant de prédire les épitopes du CMH de classe II à l'aide de la technique d'apprentissage par instances multiples. Le Separated Constructive Clustering Ensemble (SCCE) est notre nouvelle version du Constructive Clustering Ensemble (CCE). Le SCCE intègre un algorithme génétique, le clustering K-médoïdes, l'apprentissage par ensembles et une machine à vecteurs de support dans une orchestration visant à prédire les épitopes du CMH de classe II. Le SCCE a été testé sur quatre ensembles de données de référence et a atteint une précision moyenne de 85 %. Les résultats du SCCE surpassent la plupart des méthodes de régression de pointe actuelles. Le SCCE a obtenu ces résultats en utilisant uniquement des indicateurs de liaison et de non-liaison, sans avoir besoin de données de régression.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Die erfolgreiche Vorhersage von Epitopen der Moleküle des Haupthistokompatibilitätskomplexes (MHC) ist ein wesentlicher Schritt bei der Entwicklung genetischer Impfstoffe. Die erfolgreiche Vorhersage von MHC-Klasse-II-Epitopen ist schwieriger als die von MHC-Klasse-I-Epitopen, da die Klasse-II-Moleküle an beiden Enden eine offene Bindungsrinne aufweisen; diese Struktur führt zu einer variablen Länge der MHC-II-Epitope und erschwert die Identifizierung des kernbildenden 9-Mers. In diesem Buch stellen wir einen neuartigen Klassifikationsalgorithmus zur Vorhersage von MHC-Klasse-II-Epitopen vor, der auf der Multiple-Instance-Learning-Technik basiert. Das Separated Constructive Clustering Ensemble (SCCE) ist unsere neue Version des Constructive Clustering Ensemble (CCE). SCCE integriert genetische Algorithmen, K-Medoid-Clustering, Ensemble-Lernen und Support-Vektor-Maschinen in einem koordinierten Ansatz zur Vorhersage der MHC-II-Epitope. SCCE wurde anhand von vier Benchmark-Datensätzen getestet und erzielte eine durchschnittliche Genauigkeit von 85 %. Die Ergebnisse von SCCE übertreffen die meisten aktuellen Regressionsmethoden auf dem Stand der Technik. SCCE erzielte diese Ergebnisse unter Verwendung von Binder- und Non-Binder-Flags, ohne dass Regressionsdaten erforderlich waren.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Skuteczne przewidywanie epitopów cz¿steczek kompleksu g¿ównego zgodno¿ci tkankowej (MHC) stanowi kluczowy etap w projektowaniu szczepionek genetycznych. Skuteczne przewidywanie epitopów MHC klasy II jest trudniejsze ni¿ w przypadku epitopów MHC klasy I ze wzgl¿du na otwarty rowek wi¿zania na obu ko¿cach cz¿steczek klasy II; struktura ta powoduje zmienn¿ d¿ugo¿¿ epitopów MHC klasy II i utrudnia wykrycie 9-merowego rdzenia wi¿zania. W niniejszej ksi¿¿ce przedstawili¿my nowatorski algorytm klasyfikacyjny s¿u¿¿cy do przewidywania epitopów MHC klasy II z wykorzystaniem techniki uczenia wielokrotnego. Separated Constructive Clustering Ensemble (SCCE) to nasza nowa wersja algorytmu Constructive Clustering Ensemble (CCE). SCCE ¿¿czy algorytm genetyczny, klasteryzacj¿ K-medoidów, uczenie zespo¿owe oraz maszyn¿ wektorów no¿nych w ramach koordynacji maj¿cej na celu przewidywanie epitopów MHC klasy II. SCCE zostäo przetestowane na czterech zestawach danych porównawczych i osi¿gn¿¿o ¿redni¿ dok¿adno¿¿ na poziomie 85%. Wyniki SCCE przewy¿szaj¿ wi¿kszo¿¿ najnowocze¿niejszych metod regresji. SCCE osi¿gn¿¿o te wyniki, wykorzystuj¿c wy¿¿cznie flagi wi¿zania i braku wi¿zania, bez konieczno¿ci stosowania danych regresji.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Una previsione accurata degli epitopi delle molecole del Complesso Maggiore di Istocompatibilità (MHC) rappresenta un passo fondamentale nella progettazione dei vaccini genetici. Prevedere con successo gli epitopi MHC di classe II è più difficile rispetto a quelli di classe I a causa della scanalatura di legame aperta su entrambe le estremità delle molecole di classe II; questa struttura comporta una lunghezza variabile degli epitopi MHC di classe II e complica il compito di individuare il 9-mero di legame centrale. In questo libro abbiamo presentato un nuovo algoritmo di classificazione per la previsione degli epitopi MHC di classe II utilizzando la tecnica del Multiple Instance Learning. Il Separated Constructive Clustering Ensemble (SCCE) è la nostra nuova versione del Constructive Clustering Ensemble (CCE). SCCE integra l'algoritmo genetico, il clustering K-medoid, l'apprendimento ensemble e la macchina a vettori di supporto in un'orchestrazione per prevedere gli epitopi MHC II. L'SCCE è stato testato su quattro set di dati di riferimento e ha raggiunto un'accuratezza media dell'85%. I risultati dell'SCCE superano la maggior parte dei metodi di regressione all'avanguardia attualmente disponibili. L'SCCE ha ottenuto questi risultati utilizzando solo i flag di legame e di non legame, senza bisogno di dati di regressione.…