STATISTICAL DIAGNOSTICS FOR CANCER - ANALYSING HIGH-DIMENSIONAL DATA

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.

Frank Emmert-Streib 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 ?eld 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.



Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria) and at the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) leading the Insititute for Bioinformatics and Translational Research. His research interests are in bioinformatics, systems biology, complex networks and statistics. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational and systems biology.

Preface



1. Introduction





Part I: Statistical and computational methods

2. Bayesian variable selection for disease classi?cation using gene expression
data
Aijun Yang and X. Y. Song, Department of Statistics, The Chinese Univer-
sity of Hong Kong, Hong Kong, P.R.China



3. Iterative Bayesian Model Averaging: a method for the application of sur-
vival analysis to high-dimensional microarray data
Ka Yee Yeung, Department of Microbiology, Box 358070, University of
Washington, Seattle, WA 98195, USA



4. A human functional protein interaction network and its application to can-
cer data analysis
Guanming Wu, Ontario Institute for Cancer Research, MaRS Centre, South
Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada



5. Semi-supervised recursively partitioned mixture models for identifying
cancer subtypes
Devin C. Koestler, Andres Houseman, Department of Biostatistics, Harvard
School of Public Health, Boston, MA 02115, USA



6. Comparative survival analysis of breast cancer microarray studies identi-
?es important prognostic genetic pathways
Jeffrey C Miecznikowski, Department of Biostatistics, University at Buffalo
(SUNY), Buffalo, New York 14214 USA



7. Network properties of human disease genes with pleiotropic effects
Sreenivas Chavali, The Unit for Clinical Systems Biology, University of
Gothenburg, Medicinaregatan 5A, Gothenburg SE405 30, Sweden



8. Bayesian ranking and selection methods using hierarchical mixture mod-
els in microarray studies
Hisashi Noma, Department of Biostatistics, Kyoto University School of
Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan



9. Statistical analysis of the cancer cells molecular entropy using high-throughput
data
Wessel N. van Wieringen and Aad W. van der Vaart, Department of Epi-
demiology and Biostatistics, VU University Medical Center, P.O. Box 7075,
3
1007 MB Amsterdam, The Netherlands and Department of Mathematics,
VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The
Netherlands



10. Inference of hierarchical regulatory network of estrogen-dependent breast
cancer through ChIP-based data
Victor X Jin, Department of Biomedical Informatics, The Ohio State Uni-
versity, Columbus, USA



11. Discriminant and network analysis to study origin of cancer
Li Chen 1 , Ye Tian 1 , Steve G. Bova 2 , Ie-Ming Shih 2 , and Yue Wang 1,
1- Virginia Polytechnic Institute and State University, 2- Johns Hopkins Uni-
versity School of Medicine



12. Inverse perturbation for optimal intervention in gene regulatory networks
Nidhal Bouaynaya, Department of Systems Engineering, University of Arkansas
at Little Rock, Little Rock, AR 72204



13. Prediction and Testing of Biological Networks Underlying Intestinal Can-
cer
Mark R. Chance, Vishal Patel and Gurkan Bebek, Center for Proteomics &
Bioinformatics, Department of Genetics, Case Western Reserve University



Part II: Software and databases



14. DiNAMIC: A Method To Identify Recurrent DNA Copy Number Aberrations
in Tumors
Fred A. Wright, Department of Biostatistics, University of North Carolina
at Chapel Hill, Chapel Hill, NC 27599 USA



15. Public databases and visualization methods: An overview
Ricardo de Matos Simoes, Matthias Dehmer and Frank Emmert-Streib



Part III: Outlook



16. Personalized cancer diagnostics and prognostics
Frank Emmert-Streib, Matthias Dehmer and Dean Fennell