Overview
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 sufcient 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.
