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Quantitative medical data analysis using mathematical tools and statistical techniques /

Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techn...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: World Scientific (Firm)
Otros Autores: Hong, Don, Shyr, Yu
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; New Jersey : World Scientific, ©2007.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Quantitative medical data analysis using mathematical tools and statistical techniques /  |c editors, Don Hong, Yu Shyr. 
260 |a Singapore ;  |a New Jersey :  |b World Scientific,  |c ©2007. 
300 |a 1 online resource (ix, 353 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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504 |a Includes bibliographical references and index. 
505 0 |a Statistical methodology and stochastic modeling -- Proteomics and genomics -- Survival modeling and analysis -- Mathematical models for diseases -- Computing and visualization. 
520 |a Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis. This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. The topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing. 
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650 0 |a Statistics. 
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700 1 |a Hong, Don. 
700 1 |a Shyr, Yu. 
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