Full 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.
Contents
Control of Type I Error Rates for Oncology Biomarker Discovery with High-throughput Platforms (Jeffrey Miecznikowski, Dan Wang, Song Liu) Discovery of Expression Signatures in Chronic Myeloid Leukemia by Bayesian Model Averaging (Ka Yee Yeung) Bayesian Ranking and Selection Methods in Microarray Studies (Hisashi Noma, Shigeyuki Matsui) Multi-class Classification via Bayesian Variable Selection with Gene Expression Data (Yang Aijun, Song Xinyuan, Li Yunxian) Colorectal Cancer and its Molecular Subsystems: Construction, Interpretation, and Validation (Vishal N. Patel, Mark R. Chance) Semi-Supervised Methods for Analyzing High-Dimensional Genomic Data (Devin C. Koestler) Network Medicine: Disease Genes in Molecular Networks (Sreenivas Chavali, Kartiek Kanduri) Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data (Binhua Tang, Fei Gu, Victor X. Jin) Network Module Based Approaches in Cancer Data Analysis (Guanming Wu, Lincoln D. Stein) Discriminant and Network Analysis to Study Origin Of Cancer (Yue Wang, Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie-Ming Shih) Intervention and Control of Gene Regulatory Net-Works: Theoretical Framework and Application to Human Melanoma Gene Regulation (Nidhal Bouaynaya, Roman Shterenberg, Dan Schonfeld, Hassan M. Fathallah-Shaykh) Identification of Recurrent DNA Copy Number Aberrations in Tumors (Vonn Walter, Andrew B. Nobel, D. Neil Hayes, Fred A. Wright) The Cancer Cell, its Entropy, and High-Dimensional Molecular Data (Wessel N. van Wieringen, Aad W. van der Vaart) Overview of Public Cancer Databases, Resources and Visualization Tools (Frank Emmert-Streib, Ricardo de Matos Simoes, Shailesh Tripathi, Matthias Dehmer)
