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Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data9783527332625, 9783527665471

Author : M. Dehmer, Frank Emmert?Streib(eds.)
Description:This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities. Content: Chapter 1 Control of Type I Error Rates for Oncology Biomarker Discovery with High?Throughput Platforms (pages 126): Jeffrey Miecznikowski, Dan Wang and Song LiuChapter 2 Overview of Public Cancer Databases, Resources, and Visualization Tools (pages 2740): Frank Emmert?Streib, Ricardo de Matos Simoes, Shailesh Tripathi and Matthias DehmerChapter 3 Discovery of Expression Signatures in Chronic Myeloid Leukemia by Bayesian Model Averaging (pages 4155): Ka Yee YeungChapter 4 Bayesian Ranking and Selection Methods in Microarray Studies (pages 5774): Hisashi Noma and Shigeyuki MatsuiChapter 5 Multiclass Classification via Bayesian Variable Selection with Gene Expression Data (pages 7592): Yang Aijun, Song Xinyuan and Li YunxianChapter 6 Semisupervised Methods for Analyzing High?dimensional Genomic Data (pages 93106): Devin C. KoestlerChapter 7 Colorectal Cancer and Its Molecular Subsystems: Construction, Interpretation, and Validation (pages 107132): Vishal N. Patel and Mark R. ChanceChapter 8 Network Medicine: Disease Genes in Molecular Networks (pages 133151): Sreenivas Chavali and Kartiek KanduriChapter 9 Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data (pages 153171): Binhua Tang, Fei Gu and Victor X. JinChapter 10 Network?Module?Based Approaches in Cancer Data Analysis (pages 173192): Guanming Wu and Lincoln SteinChapter 11 Discriminant and Network Analysis to Study Origin of Cancer (pages 193214): Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie?Ming Shih and Yue WangChapter 12 Intervention and Control of Gene Regulatory Networks: Theoretical Framework and Application to Human Melanoma Gene Regulation (pages 215238): Nidhal Bouaynaya, Roman Shterenberg, Dan Schonfeld and Hassan M. Fathallah?ShaykhChapter 13 Identification of Recurrent DNA Copy Number Aberrations in Tumors (pages 239260): Vonn Walter, Andrew B. Nobel, D. Neil Hayes and Fred A. WrightChapter 14 The Cancer Cell, Its Entropy, and High?Dimensional Molecular Data (pages 261285): Wessel N. van Wieringen and Aad W. van der Vaart
Categories: Biology Biostatistics
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Language : English
N° Of Pages : 312
File Info : pdf 8 Mb

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