E-Book Overview
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
E-Book Content
Edited by Frank Emmert-Streib and Matthias Dehmer Statistical Diagnostics for Cancer
Titles of the Series “Quantitative and Network Biology” Volume 1 Dehmer, M., Emmert-Streib, F., Graber, A., Salvador, A. (eds.)
Applied Statistics for Network Biology Methods in Systems Biology 2011 ISBN: 978-3-527-32750-8
Volume 2 Dehmer, M., Varmuza, K., Bonchev, D.(eds.)
Statistical Modelling of Molecular Descriptors in QSAR/QSPR 2012 ISBN: 978-3-527-32434-7
Related Titles Zhou, X.-H., Obuchowski, N. A., McClish, D. K.
Statistical Methods in Diagnostic Medi