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STATISTICAL DIAGNOSTICS FOR CANCER: HIGH-DIMNSIONL DATA

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The title discusses different methods to statistically analyze and validate data created with high-throughput methods. In contrast to other books the title focusses on systems approaches i.e. no single gene or protein is the basic of the analysis but a more or less complex biological network. From a methodological point of view the chapters in this book will describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. In contrast to classic approaches, these methods will focus on a systems level. That means instead of putting for example ?a gene? in the center of the investigation, interacting groups of genes are interrogated systematically leading to a systems approaches appropriate for the analysis of complex diseases in general. Further, with the availability of suf¿cient computer power in recent years the attention shifted from parametric to nonparametric methods The later being much more demanding in terms of computational prowess). For this reason, the presented methods make use of computer intensive approaches, like Bootstrap, Markov Chain Monte Carlo (MCMC) or general resampling methods. Also, with the establishment of many public databases often prior information is available that can be utilized. Therefore a chapter on Bayesian methods is included and provide a systematic means to integrate this information.

Statistical Diagnostics for Cancer

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Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Contents.
Preface.
List of Contributors.
1: General Overview.
2: Control of Type I Error Rates for Oncology Biomarker Discovery with High-Throughput Platforms.
3: Overview of Public Cancer Databases, Resources, and Visualization Tools.
4: Bayesian Methods.
5: Discovery of Expression Signatures in Chronic Myeloid Leukemia by Bayesian Model Averaging.
6: Bayesian Ranking and Selection Methods in Microarray Studies.
7: Multiclass Classification Via Bayesian Variable Selection with Gene Expression Data.
8: Semisupervised Methods for Analyzing High-dimensional Genomic Data.
9: Network-based Approaches.
10: Colorectal Cancer and Its Molecular Subsystems: Construction, Interpretation, and Validation.
11: Network Medicine: Disease Genes in Molecular Networks.
12: Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data.
13: Network-module-based Approaches in Cancer Data Analysis.
14: Discriminant and Network Analysis to Study Origin of Cancer.
15: Intervention and Control of Gene Regulatory Networks: Theoretical Framework and Application to Human Melanoma Gene Regulation.
16: Phenotype Influence of DNA Copy Number Aberrations.
17: Identification of Recurrent DNA Copy Number Aberrations in Tumors.
18: The Cancer Cell, Its Entropy, and High-Dimensional Molecular Data.
Index.

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Statistical Diagnostics for Cancer