Bayesian Modeling in Bioinformatics
Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis.
The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping.
Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.
Estimation and Testing in Time-Course Microarray Experiments, C. Angelini, D. De Canditilis, and M. Pensky
Classification for Differential Gene Expression Using Bayesian Hierarchical Models, N. Bochkina and A. Lewin
Applications of the Mode Oriented Stochastic Search (MOSS) for Discrete Multi-Way Data to Genome-Wide Studies, A. Dobra, L. Briollais, H. Jarjanazi, H. Ozelik, and H. Massam
Nonparametric Bayesian Bioinformatics, D. Dunson
Measurement Error Models for cDNA Microarray and Time-to-Event Data with Applications to Breast Cancer, J. Gelfond and J. Ibrahim
Bayesian Robust Inference for Differential Gene Expression, R. Gottardo
Bayesian Hidden Markov Modeling of Array CGH Data, S. Guha
Recent Developments in Bayesian Phylogenetics, M. Holder, J. Sukumaran, and R. Brown
Gene Selection for the Identification of Biomarkers in High-Throughput Data, J. Jeong, M. Vannucci, K. Do, B. Broom, S. Kim, N. Sha, M. Tadese, K. Yan, and L. Puzstai
Sparsity Priors for Protein-Protein Interaction Predictions, I. Kim, Y. Liu, and H. Zhao
Learning Bayesian Networks for Gene Expression Data, F. Liang
In Vitro to In Vivo Factor Profiling in Expression Genomics, J. Lucas, C, Carvalho, D. Merl, and M. West
Proportional Hazards Regression Using Bayesian Kernel Machines, A. Maity and B. Mallick
A Bayesian Mixture Model for Protein Biomarker Discovery, P. Muller, K. Baggerly, K. Do, and Bandopadhyay
Bayesian Methods for Detecting Differentially Expressed Genes, F. Yu, M-H. Chen, and L. Kuo
Bayes and Empirical Bayes Methods for Spotted Microarray Data Analysis, D. Zhang
Bayesian Classification Method for QTL Mapping, M. Zhang
"… an excellent source of reference for the application of Bayesian methodologies in bioinformatics data, as it provides a wide range of simulated and real data examples along with information on the most recent open source software packages available and the links to the code for implementing the methods described in each chapter. Moreover, the clear and comprehensive explanations provided for the technical concepts covered and the extended list of references at the end of each chapter make this book a useful learning and educational tool. … a substantial contribution to the field of Bayesian statistics and should be in the collection of any scientist engaged with advanced statistical technologies in the area of bioinformatics."
—Vasilis Nikolaou, Statistical Methods in Medical Research, 21(6), 2012
"New topics are explicitly and carefully introduced and the articles would be easy to read for researchers and graduate students. … This book would be an excellent reference for researchers and graduate students interested in learning about recently developed Bayesian approaches to genomic and proteomic data."
—Hisashi Noma, Biometrics, December 2012
"This book offers a peek into the world of bioinformatics — the intricate data structures and challenging questions posed to bioinformaticians. … The book showcases what Bayesian methods offer bioinformatics high-throughput data. …The book editors are leading Bayesians and have assembled a broad collection of articles authored by distinguished Bayesian researchers. … A few articles introduce modern Bayesian approaches relevant for high-throughput data. These include well-written articles reviewing Dirichlet process priors and Bayesian kernel machines that bioinformatics researchers will enjoy. … a delightful plate of appetizers introducing the world of Bayesian bioinformatics. Bon appétit!"
—Śaunak Sen, Australian & New Zealand Journal of Statistics, 2012
"All the papers are well written, providing a good entry into the subject matter issues as well as Bayesian issues like choice of likelihood and prior. The biggest strength of the book is the variety of problems that can be addressed through microarray experiments. … this is a remarkable survey of different types of microarray data and analysis of such data."
—Jayanta K. Ghosh, International Statistical Review, 2012
"Overall, the book is an interesting mix of methodology and applications relevant to bioinformatics. A particularly appealing feature is that many of the chapters use freely available datasets and software, providing links to obtain these. … I can recommend the book for anyone wishing to dip into a few of the interesting areas of current research in the field; the book has certainly whetted my appetite to explore some areas further."
—Matthew Sperrin, ISCB News, 52, December 2011