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

doi:10.1017/CBO9781139226448

Advances in Statistical Bioinformatics

Models and Integrative Inference for High-Throughput Data

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Chekouo, Thierry Stingo, Francesco C. Doecke, James D. and Do, Kim-Anh 2015. miRNA–Target Gene Regulatory Networks: A Bayesian Integrative Approach to Biomarker Selection with Application to Kidney Cancer. Biometrics, Vol. 71, Issue. 2, p. 428.

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Cotterill, Amy and Jaki, Thomas 2018. Dose‐escalation strategies which use subgroup information. Pharmaceutical Statistics, Vol. 17, Issue. 5, p. 414.

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Fernandez-Lozano, Carlos Carballal, Adrian Machado, Penousal Santos, Antonino and Romero, Juan 2019. Visual complexity modelling based on image features fusion of multiple kernels. PeerJ, Vol. 7, Issue. , p. e7075.

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Vardaxis, Ioannis Drabløs, Finn Rye, Morten B and Lindqvist, Bo Henry 2020. MACPET: model-based analysis for ChIA-PET. Biostatistics, Vol. 21, Issue. 3, p. 625.

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Zhang, Yunwei Wong, Germaine Mann, Graham Muller, Samuel and Yang, Jean Y H 2022. SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data. GigaScience, Vol. 11, Issue. ,

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Edited by Kim-Anh Do, University of Texas, MD Anderson Cancer Center, Zhaohui Steve Qin, Emory University, Atlanta, Marina Vannucci, Rice University, Houston

Publisher:: Cambridge University Press
Online publication date:: June 2013
Print publication year:: 2013
Online ISBN:: 9781139226448
DOI:: https://doi.org/10.1017/CBO9781139226448

Subjects:: Genomics, Bioinformatics and Systems Biology, Statistics for Life Sciences, Medicine and Health, Life Sciences, Statistics and Probability

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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.

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Advances in Statistical Bioinformatics

Models and Integrative Inference for High-Throughput Data

This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Frontmatter
pp i-iv

Contents
pp v-vi

List of Contributors
pp vii-x

Preface
pp xi-xvi

1 - An Introduction to Next-Generation Biological Platforms
pp 1-30

2 - An Introduction to The Cancer Genome Atlas
pp 31-53

3 - DNA Variant Calling in Targeted Sequencing Data
pp 54-76

4 - Statistical Analysis of Mapped Reads from mRNA-Seq Data
pp 77-104

5 - Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
pp 105-125

6 - Bayesian Model-Based Approaches for Solexa Sequencing Data
pp 126-137

7 - Statistical Aspects of ChIP-Seq Analysis
pp 138-169

8 - Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
pp 170-187

9 - Multivariate Linear Models for GWAS
pp 188-207

10 - Bayesian Model Averaging for Genetic Association Studies
pp 208-223

11 - Whole-Genome Multi-SNP-Phenotype Association Analysis
pp 224-243

12 - Methods for the Analysis of Copy Number Data in Cancer Research
pp 244-271

13 - Bayesian Models for Integrative Genomics
pp 272-291

14 - Bayesian Graphical Models for Integrating Multiplatform Genomics Data
pp 292-311

15 - Genetical Genomics Data: Some Statistical Problems and Solutions
pp 312-330

16 - A Bayesian Framework for Integrating Copy Number and Gene Expression Data
pp 331-349

17 - Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
pp 350-365

18 - Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
pp 366-381

19 - Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
pp 382-397

20 - Optimized Cross-Study Analysis of Microarray-Based Predictors
pp 398-422

21 - Functional Enrichment Testing: A Survey of Statistical Methods
pp 423-444

22 - Discover Trend and Progression Underlying High-Dimensional Data
pp 445-459

23 - Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
pp 460-476

Index
pp 477-481

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Advances in Statistical Bioinformatics

Models and Integrative Inference for High-Throughput Data

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