Details

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

<b>Frank Emmert-Streib</b> studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments. <br /><br /><b>Matthias Dehmer</b> studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology and University of Coimbra (Portugal). Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry.

This ready reference discusses different methods for statistically analyzing and validating cancer data generated from high-throughput methods. In contrast to other titles, this book focuses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or<br />less complex biological network. Due to the complex nature of cancer, such approaches are very appropriate.<br /><br />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 suffi cient computer power in the recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches such as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, Bayesian methods are discussed, because these are particularly useful for a systematic data integration. Thus, the book serves as a useful guide for methods-oriented scientists and the medical and basic research community.

SG