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Advances in Statistical Bioinformatics
Models and Integrative Inference for High-Throughput Data

£113.00

Virginia Mohlere, Wenting Wang, Ganiraju Manyam, Bradley M. Broom, Rehan Akbani, Wenyi Wang, Yu Fan, Terence P. Speed, Ernest Turro, Alex Lewin, Zhaonan Sun, Han Wu, Yu Zhu, Riten Mitra, Peter Mueller, Yuan Ji, Jonathan Cairns, Andy G. Lynch, Simon Tavare, Raphael Gottardo, Sangsoon Woo, Chiara Sabatti, Christine Peterson, Michael Swartz, Sanjay Shete, Marina Vannucci, Yongtao Guan, Kai Wang, Kim-Anh Do, Melissa Bondy, Patricia Thompson, Kevin Coombes, Francesco C. Stingo, Veerabhadran Baladandayuthapani, Chris C. Holmes, Hongzhe Li, Filippo Trentini, Peter Muller, Haisu Ma, Hongyu Zhao, Keegan Korthauer, John Dawson, Christina Kendziorski, Brent A. Johnson, Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani, Laila M. Poisson, Peng Qiu, Jennifer A. Tom, Janet S. Sinsheimer, Marc A. Suchard
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  • Date Published: July 2013
  • availability: Available
  • format: Hardback
  • isbn: 9781107027527

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  • Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.

    • Describes statistical methods and computational tools for the integration and analysis of different types of molecular data generated in biomedical research studies
    • Written for statisticians interested in modeling and analyzing molecular data who are not necessarily experts in this area
    • Has a strong focus on applications in cancer research that further the development of personalized medicine by taking into account specific clinical and genetic information for each patient
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    Product details

    • Date Published: July 2013
    • format: Hardback
    • isbn: 9781107027527
    • length: 514 pages
    • dimensions: 229 x 152 x 29 mm
    • weight: 0.79kg
    • contains: 120 b/w illus. 17 colour illus. 20 tables
    • availability: Available
  • Table of Contents

    1. An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang and Ganiraju Manyam
    2. An introduction to the cancer genome atlas Bradley M. Broom and Rehan Akbani
    3. DNA variant calling in targeted sequencing data Wenyi Wang, Yu Fan and Terence P. Speed
    4. Statistical analysis of mapped reads from mRNA-seq data Ernest Turro and Alex Lewin
    5. Model-based methods for transcript expression level quantification in RNA-seq Zhaonan Sun, Han Wu and Yu Zhu
    6. Bayesian model-based approaches for solexa sequencing data Riten Mitra, Peter Mueller and Yuan Ji
    7. Statistical aspects of ChIP-seq analysis Jonathan Cairns, Andy G. Lynch and Simon Tavare
    8. Bayesian modeling of ChIP-seq data from transcription factor to nucleosome positioning Raphael Gottardo and Sangsoon Woo
    9. Multivariate linear models for GWAS Chiara Sabatti
    10. Bayesian model averaging for genetic association studies Christine Peterson, Michael Swartz, Sanjay Shete and Marina Vannucci
    11. Whole-genome multi-SNP-phenotype association analysis Yongtao Guan and Kai Wang
    12. Methods for the analysis of copy number data in cancer research Bradley M. Broom, Kim-Anh Do, Melissa Bondy, Patricia Thompson and Kevin Coombes
    13. Bayesian models for integrative genomics Francesco C. Stingo and Marina Vannucci
    14. Bayesian graphical models for integrating multiplatform genomics data Wenting Wang, Veerabhadran Baladandayuthapani, Chris C. Holmes and Kim-Anh Do
    15. Genetical genomics data: some statistical problems and solutions Hongzhe Li
    16. A Bayesian framework for integrating copy number and gene expression data Yuan Ji, Filippo Trentini and Peter Muller
    17. Application of Bayesian sparse factor analysis models in bioinformatics Haisu Ma and Hongyu Zhao
    18. Predicting cancer subtypes using survival-supervised latent Dirichlet allocation models Keegan Korthauer, John Dawson and Christina Kendziorski
    19. Regularization techniques for highly correlated gene expression data with unknown group structure Brent A. Johnson
    20. Optimized cross-study analysis of microarray-based predictors Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson and Giovanni Parmigiani
    21. Functional enrichment testing: a survey of statistical methods Laila M. Poisson
    22. Discover trend and progression underlying high-dimensional data Peng Qiu
    23. Bayesian phylogenetics adapts to comprehensive infectious disease sequence data Jennifer A. Tom, Janet S. Sinsheimer and Marc A. Suchard.

  • Editors

    Kim-Anh Do, University of Texas, MD Anderson Cancer Center
    Dr Kim-Anh Do is a Professor of Biostatistics, ad interim Head of the Division of Quantitative Sciences, and ad interim Chair of the Department of Biostatistics at the University of Texas MD Anderson Cancer Center.

    Zhaohui Steve Qin, Emory University, Atlanta
    Zhaohui Steve Qin is an Associate Professor in the Department of Biostatistics and Bioinformatics at the Rollins School of Public Health, Emory University.

    Marina Vannucci, Rice University, Houston
    Dr Marina Vannucci is currently a Professor in the Department of Statistics and Director of the Interinstitutional Graduate Program in Biostatistics at Rice University and an adjunct faculty member of the University of Texas MD Anderson Cancer Center.

    Contributors

    Virginia Mohlere, Wenting Wang, Ganiraju Manyam, Bradley M. Broom, Rehan Akbani, Wenyi Wang, Yu Fan, Terence P. Speed, Ernest Turro, Alex Lewin, Zhaonan Sun, Han Wu, Yu Zhu, Riten Mitra, Peter Mueller, Yuan Ji, Jonathan Cairns, Andy G. Lynch, Simon Tavare, Raphael Gottardo, Sangsoon Woo, Chiara Sabatti, Christine Peterson, Michael Swartz, Sanjay Shete, Marina Vannucci, Yongtao Guan, Kai Wang, Kim-Anh Do, Melissa Bondy, Patricia Thompson, Kevin Coombes, Francesco C. Stingo, Veerabhadran Baladandayuthapani, Chris C. Holmes, Hongzhe Li, Filippo Trentini, Peter Muller, Haisu Ma, Hongyu Zhao, Keegan Korthauer, John Dawson, Christina Kendziorski, Brent A. Johnson, Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani, Laila M. Poisson, Peng Qiu, Jennifer A. Tom, Janet S. Sinsheimer, Marc A. Suchard

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