This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc.
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This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc.
Dr. Yingjiee Hu received his Master degree (Hons) and PhD in Computer and Information Sciences from Auckland University of Technology, New Zealand. He is currently a postdoctoral research fellow in KEDRI at AUT. His research interests are in the areas of neural network computing, data mining techniques, personalized modelling and bioinformatics.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc. 300 pp. Englisch. Nº de ref. del artículo: 9783844328530
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Librería: moluna, Greven, Alemania
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hu YingjieDr. Yingjiee Hu received his Master degree (Hons) and PhD in Computer and Information Sciences from Auckland University of Technology, New Zealand. He is currently a postdoctoral research fellow in KEDRI at AUT. His researc. Nº de ref. del artículo: 5473253
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Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Personalised Modelling Framework and Systems for Data Analysis | Integrated Optimisation Method based on Personalised Modelling for Genomic Data Analysis and Biomedical Applications | Yingjie Hu | Taschenbuch | 300 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844328530 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Nº de ref. del artículo: 107054329
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 300 pp. Englisch. Nº de ref. del artículo: 9783844328530
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc. Nº de ref. del artículo: 9783844328530
Cantidad disponible: 1 disponibles