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035    (MiAaPQ)EBC514371 
035    (Au-PeEL)EBL514371 
035    (CaPaEBR)ebr10383627 
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050  4 QH324.2 -- .S725 2010eb 
082 0  570.285 
100 1  Lee, Jae K 
245 10 Statistical Bioinformatics :|bFor Biomedical and Life 
       Science Researchers 
250    1st ed 
264  1 Hoboken :|bJohn Wiley & Sons, Incorporated,|c2014 
264  4 |c©2010 
300    1 online resource (386 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
505 0  Intro -- STATISTICAL BIOINFORMATICS -- CONTENTS -- PREFACE
       -- CONTRIBUTORS -- 1 ROAD TO STATISTICAL BIOINFORMATICS --
       Challenge 1: Multiple-Comparisons Issue -- Challenge 2: 
       High-Dimensional Biological Data -- Challenge 3: Small-n 
       and Large-p Problem -- Challenge 4: Noisy High-Throughput 
       Biological Data -- Challenge 5: Integration of Multiple, 
       Heterogeneous Biological Data Information -- References --
       2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING 
       LARGE BIOLOGICAL DATA -- 2.1 Introduction -- 2.2 Basic 
       Concepts -- 2.3 Conditional Probability and Independence -
       - 2.4 Random Variables -- 2.5 Expected Value and Variance 
       -- 2.6 Distributions of Random Variables -- 2.7 Joint and 
       Marginal Distribution -- 2.8 Multivariate Distribution -- 
       2.9 Sampling Distribution -- 2.10 Summary -- 3 QUALITY 
       CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA -- 3.1 Sources 
       of Error in High-Throughput Biological Experiments -- 3.2 
       Statistical Techniques for Quality Control -- 3.3 Issues 
       Specific to Microarray Gene Expression Experiments -- 3.4 
       Conclusion -- References -- 4 STATISTICAL TESTING AND 
       SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS -- 4.1 
       Introduction -- 4.2 Statistical Testing -- 4.3 Error 
       Controlling -- 4.4 Real Data Analysis -- 4.5 Concluding 
       Remarks -- Acknowledgments -- References -- 5 CLUSTERING: 
       UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA -- 5.1 
       Measures of Similarity -- 5.2 Clustering -- 5.3 Assessment
       of Cluster Quality -- 5.4 Conclusion -- References -- 6 
       CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL 
       BIOLOGICAL DATA -- 6.1 Introduction -- 6.2 Classification 
       and Prediction Methods -- 6.3 Feature Selection and 
       Ranking -- 6.4 Cross-Validation -- 6.5 Enhancement of 
       Class Prediction by Ensemble Voting Methods -- 6.6 
       Comparison of Classification Methods Using High-
       Dimensional Data -- 6.7 Software Examples for 
       Classification Methods 
505 8  References -- 7 MULTIDIMENSIONAL ANALYSIS AND 
       VISUALIZATION ON LARGE BIOMEDICAL DATA -- 7.1 Introduction
       -- 7.2 Classical Multidimensional Visualization Techniques
       -- 7.3 Two-Dimensional Projections -- 7.4 Issues and 
       Challenges -- 7.5 Systematic Exploration of Low-
       Dimensional Projections -- 7.6 One-Dimensional Histogram 
       Ordering -- 7.7 Two-Dimensional Scatterplot Ordering -- 
       7.8 Conclusion -- References -- 8 STATISTICAL MODELS, 
       INFERENCE, AND ALGORITHMS FOR LARGE BIOLOGICAL DATA 
       ANALYSIS -- 8.1 Introduction -- 8.2 Statistical/
       Probabilistic Models -- 8.3 Estimation Methods -- 8.4 
       Numerical Algorithms -- 8.5 Examples -- 8.6 Conclusion -- 
       References -- 9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT 
       BIOLOGICAL EXPERIMENTS -- 9.1 Randomization -- 9.2 
       Replication -- 9.3 Pooling -- 9.4 Blocking -- 9.5 Design 
       for Classifications -- 9.6 Design for Time Course 
       Experiments -- 9.7 Design for eQTL Studies -- References -
       - 10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE 
       BIOLOGICAL DATA ANALYSIS -- 10.1 Introduction -- 10.2 
       Resampling Methods for Prediction Error Assessment and 
       Model Selection -- 10.3 Feature Selection -- 10.4 
       Resampling-Based Classification Algorithms -- 10.5 
       Practical Example: Lymphoma -- 10.6 Resampling Methods -- 
       10.7 Bootstrap Methods -- 10.8 Sample Size Issues -- 10.9 
       Loss Functions -- 10.10 Bootstrap Resampling for 
       Quantifying Uncertainty -- 10.11 Markov Chain Monte Carlo 
       Methods -- 10.12 Conclusions -- References -- 11 
       STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND 
       PATHWAYS -- 11.1 Introduction -- 11.2 Boolean Network 
       Modeling -- 11.3 Bayesian Belief Network -- 11.4 Modeling 
       of Metabolic Networks -- References -- 12 TRENDS AND 
       STATISTICAL CHALLENGES IN GENOMEWIDE ASSOCIATION STUDIES -
       - 12.1 Introduction -- 12.2 Alleles, Linkage 
       Disequilibrium, and Haplotype -- 12.3 International HapMap
       Project -- 12.4 Genotyping Platforms 
505 8  12.5 Overview of Current GWAS Results -- 12.6 Statistical 
       Issues in GWAS -- 12.7 Haplotype Analysis -- 12.8 
       Homozygosity and Admixture Mapping -- 12.9 Gene × Gene 
       and Gene × Environment Interactions -- 12.10 Gene and 
       Pathway-Based Analysis -- 12.11 Disease Risk Estimates -- 
       12.12 Meta-Analysis -- 12.13 Rare Variants and Sequence-
       Based Analysis -- 12.14 Conclusions -- Acknowledgments -- 
       References -- 13 R AND BIOCONDUCTOR PACKAGES IN 
       BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY -- 13.1 
       Introduction -- 13.2 Brief overview of the Bioconductor 
       Project -- 13.3 Experimental Data -- 13.4 Annotation -- 
       13.5 Models of Biological Systems -- 13.6 Conclusion -- 
       13.7 Acknowledgments -- References -- INDEX 
520    A practical introduction to the underlying statistical 
       concepts and techniques for successful use of 
       bioinformatics tools  Effective use of the tools and 
       methods of bioinformatics requires a careful understanding
       of not only the relevant biology and computational 
       problems, but also critical statistical principles. 
       Statistical Bioinformatics provides an essential 
       understanding of the novel statistical concepts necessary 
       for the analysis of genomic and proteomic data using 
       various bioinformatics and computational techniques. Dr. 
       Jae Lee and the authors present both basic and advanced 
       topics, focusing on those that are relevant to the 
       efficient and rigorous analysis of large data sets in 
       biology.   The book starts with an introduction to 
       probability and statistics for genome-wide data, and moves
       into topics such as clustering, classification, 
       multidimensional visualization, experimental design, 
       statistical resampling, and statistical network analysis. 
       Chapters begin with a description of a statistical concept
       and practical examples from biomedical research, followed 
       by more detailed presentation, discussion of limitations, 
       and problems.     Clearly explains the use of 
       bioinformatics tools in life sciences research without 
       requiring an advanced background in math/statistics     
       Enables biomedical and life sciences researchers to 
       successfully evaluate the validity of their results and 
       make inferences     Enables statistical and quantitative 
       researchers to rapidly learn novel statistical concepts 
       and techniques appropriate for large biological data 
       analysis     Carefully revisits frequently used 
       statistical approaches and highlights their limitations in
       large biological data analysis     Offers programming 
       examples and datasets     Includes chapter problem sets, a
       glossary, a list of statistical notations, and appendices 
       with references to background 
520 8  mathematical and technical material     Features 
       supplementary materials, including datasets, links, and a 
       statistical package available online     Statistical 
       Bioinformatics is an ideal textbook for students in 
       medicine, life sciences, and bioengineering, aimed at 
       researchers who utilize computational tools for the 
       analysis of genomic, proteomic, and many other emerging 
       high-throughput molecular data. It may also serve as a 
       rapid introduction to the bioinformatics science for 
       statistical and computational students and audiences who 
       have not experienced such analysis tasks before 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Bioinformatics -- Statistical methods.;Biology -- Data 
       processing 
655  4 Electronic books 
776 08 |iPrint version:|aLee, Jae K.|tStatistical Bioinformatics 
       : For Biomedical and Life Science Researchers|dHoboken : 
       John Wiley & Sons, Incorporated,c2014|z9780471692720 
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