| 1 An Introduction to High-Throughput Bioinformatice Data 2 Hierarchical Mixture Models for Expression Profiles 3 Bayesian Hierarchical Models for Inference in Microarray Data 4 Bayesian Process-Based Modeling of Two-Channel Microarray Experimenst:Estimating Absolute mRNA Concentrations 5 Identification of Biomarkers in Classification and Clustering 6 Modeling Nonlinear Gene Interactions Using Bayesian MARAS 7 Models for Probability of Under-and Overexression:The POE Scale 8 Sparse Statistical Modelling in Gene Expression Genomics 9 Bayesian Analysis of Cell Cycle Gene Expression Data 10 Model-Based Clustering for Expression Data Via a Dirichlet Process Mixture Model 11 Interval Mapping for Expression Quantitative Trait Loci 12 Bayesian Mixture Models for Gene Expression and Protein Profiles 13 Shrinkage Estimation for SAGE Data Using a MIXTURE Dirichlet Prior 14 Analysis of mass Sectrometry Data Using Bayesian 15 Nonparametric Models for Proteomic Peak Identification and Quantification 16 Bayesian Modeling and Inference for Sequence Motif Discovery 17 Identification of DNA Reulatory Motifs and Regulators by Integration Gene Expression and Sequence data 18 A Misclassification Model for Inferring Transcriptional Reglatory Networks 19 Estimating Cellular Signaling from Transcription Data 20 Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data 21 Bayesian Networks and Informative Priors:Transcriptional Regulatory Network Models 22 Sample Size Choice for Microarray Experiments |
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