Statistical Diagnostics For Cancer Analyzing Highdimensional Data M Dehmer by M. Dehmer, Frank Emmert?streib(eds.) 9783527332625, 9783527665471, 3527332626, 3527665471 instant download after payment.

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 1–26): Jeffrey Miecznikowski, Dan Wang and Song Liu
Chapter 2 Overview of Public Cancer Databases, Resources, and Visualization Tools (pages 27–40): Frank Emmert?Streib, Ricardo de Matos Simoes, Shailesh Tripathi and Matthias Dehmer
Chapter 3 Discovery of Expression Signatures in Chronic Myeloid Leukemia by Bayesian Model Averaging (pages 41–55): Ka Yee Yeung
Chapter 4 Bayesian Ranking and Selection Methods in Microarray Studies (pages 57–74): Hisashi Noma and Shigeyuki Matsui
Chapter 5 Multiclass Classification via Bayesian Variable Selection with Gene Expression Data (pages 75–92): Yang Aijun, Song Xinyuan and Li Yunxian
Chapter 6 Semisupervised Methods for Analyzing High?dimensional Genomic Data (pages 93–106): Devin C. Koestler
Chapter 7 Colorectal Cancer and Its Molecular Subsystems: Construction, Interpretation, and Validation (pages 107–132): Vishal N. Patel and Mark R. Chance
Chapter 8 Network Medicine: Disease Genes in Molecular Networks (pages 133–151): Sreenivas Chavali and Kartiek Kanduri
Chapter 9 Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data (pages 153–171): Binhua Tang, Fei Gu and Victor X. Jin
Chapter 10 Network?Module?Based Approaches in Cancer Data Analysis (pages 173–192): Guanming Wu and Lincoln Stein
Chapter 11 Discriminant and Network Analysis to Study Origin of Cancer (pages 193–214): Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie?Ming Shih and Yue Wang
Chapter 12 Intervention and Control of Gene Regulatory Networks: Theoretical Framework and Application to Human Melanoma Gene Regulation (pages 215–238): Nidhal Bouaynaya, Roman Shterenberg, Dan Schonfeld and Hassan M. Fathallah?Shaykh
Chapter 13 Identification of Recurrent DNA Copy Number Aberrations in Tumors (pages 239–260): Vonn Walter, Andrew B. Nobel, D. Neil Hayes and Fred A. Wright
Chapter 14 The Cancer Cell, Its Entropy, and High?Dimensional Molecular Data (pages 261–285): Wessel N. van Wieringen and Aad W. van der Vaart