Advances in Statistical Bioinformatics
-10%
portes grátis
Advances in Statistical Bioinformatics
Models and Integrative Inference for High-Throughput Data
Do, Kim-Anh; Qin, Zhaohui Steve; Vannucci, Marina
Cambridge University Press
06/2013
514
Dura
Inglês
9781107027527
15 a 20 dias
790
Descrição não disponível.
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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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
