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 book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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