Inhaltsangabe
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.
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Über die Autorin bzw. den Autor
Hossam 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 Science in the Arab Academy for Science, Technology and Maritime Transport, Egypt.
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Suchergebnisse für Immunological Bioinformatics: Predicting MHC Class...
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Immunological Bioinformatics
Verlag:
LAP LAMBERT Academic Publishing Okt 2015, 2015
ISBN 10: 3659792489
ISBN 13: 9783659792489
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch. Bestandsnummer des Verkäufers 9783659792489
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Immunological Bioinformatics
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659792489
ISBN 13: 9783659792489
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Zustand: 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 . Bestandsnummer des Verkäufers 158429523
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Immunological Bioinformatics
Verlag:
LAP LAMBERT Academic Publishing Okt 2015, 2015
ISBN 10: 3659792489
ISBN 13: 9783659792489
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Bestandsnummer des Verkäufers 9783659792489
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Immunological Bioinformatics : Predicting MHC Class II Epitopes
Verlag:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659792489
ISBN 13: 9783659792489
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783659792489
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Immunological Bioinformatics | Predicting MHC Class II Epitopes
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659792489
ISBN 13: 9783659792489
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Taschenbuch. Zustand: Neu. Immunological Bioinformatics | Predicting MHC Class II Epitopes | Hossam Elsemellawy (u. a.) | Taschenbuch | 104 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659792489 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 104146853
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