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Statistical Bioinformatics with R

Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.

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Basics of Bioinformatics

in Computational Genomics, Statistical Methods in Bioinformatics, Algorithms in Computational Biology, Multivariate Statistical Methods in Bioinformatics;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Next Generation Microarray Bioinformatics

current computational and statistical methods to analyze and interpreting both microarray and NGS data. These methods and techniques include;

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Advances In Multivariate Statistical Methods

This volume contains a collection of research articles on multivariate statistical methods, encompassing both theoretical advances and;

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Gene-Environment Interaction Analysis: Methods in Bioinformatics and Computational Biology

Gene-environment (G x E) interaction analysis is a statistical method for clarifying G x E interactions applicable to a phenotype or a;

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Bioinformatics Research and Applications

; phylogenetics; sequence analysis; and statistical methods.;

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Introduction to Bioinformatics

can help solve biological problems. Requiring only a limited mathematical and statistical background, the book shows how to efficiently apply;

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Introduction to Bioinformatics

can help solve biological problems. Requiring only a limited mathematical and statistical background, the book shows how to efficiently apply;

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Bioinformatics

This title includes a number of Open Access chapters. The book introduces bioinformatic and statistical methodology and shows;

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Clustering in Bioinformatics and Drug Discovery

drug discovery. Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory;

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Clustering in Bioinformatics and Drug Discovery

drug discovery. Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory;

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Knowledge Based Bioinformatics

to genomic and proteomic research. This book discussesknowledge-based and statistical approaches, along with applicationsin bioinformatics;

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Bioinformatics

This title includes a number of Open Access chapters. The book introduces bioinformatic and statistical methodology and shows approaches;

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Bayesian Inference for Gene Expression and Proteomics

data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high;

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Bayesian Inference For Gene Expression And Proteomics

data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high;

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New Developments In Biostatistics And Bioinformatics

, statistical methods for epidemiology, and bioinformatics.;

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Bioinformatics and Molecular Evolution

, biological databases, pattern recognition, and applications to genomics, microarrays, and proteomics emphasizes the theoretical and statistical;

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Microarray Bioinformatics

, data normalization, and missing value imputation, as well as the statistical analysis of the data and the most popular computer tools to;

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Proteome Bioinformatics

functions and their application in the field of proteomics, statistical evaluation of labeled comparative proteomics using permutation testing, and;

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Bayesian Analysis of Gene Expression Data

gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists;

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Bioinformatics and Computational Biology

, sequence alignment, molecular evolution, next-generation sequencing, systems biology, and statistical computing using R. The book also presents a;

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Fundamentals of Bioinformatics

Bioinformatics is an upcoming discipline of Life Sciences. It is an integration of computer science, and mathematical and statistical;

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Statistical Methods in Bioinformatics

generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are;

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Methodology in Robust and Nonparametric Statistics

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from;

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Methodology in Robust and Nonparametric Statistics

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from;

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