Bioinformatics for Omics Data

Omics Technologies, Data, and Bioinformatics Principles.- Data Standards for Omics Data: The Basis of Data Sharing and Reuse.- Omics Data Management and Annotation.- Data and Knowledge Management in Cross-Omics Research Projects.- Statistical Analysis Principles for Omics Data.- Statistical Methods and Models for Bridging Omics Data Levels.- Analysis of Time Course Omics Datasets.- The Use and Abuse of -Omes.- Computational Analysis of High Throughput Sequencing Data.- Analysis of Single Nucleotide Polymorphisms in Case-Control Studies.- Bioinformatics for Copy Number Variation Data.- Processing ChIP-chip Data: From the Scanner to the Browser.- Insights Into Global Mechanisms and Disease by Gene Expression Profiling.- Bioinformatics for RNomics.- Bioinformatics for Qualitative and Quantitative Proteomics.- Bioinformatics for Mass Spectrometry-Based Metabolomics.- Computational Analysis Workflows for Omics Data Interpretation.- Integration, Warehousing, and Analysis Strategies of Omics Data.- Integrating Omics Data for Signaling Pathways, Interactome Reconstruction, and Functional Analysis.- Network Inference from Time-Dependent Omics Data.- Omics and Literature Mining.- Omics-Bioinformatics in the Context of Clinical Data.- Omics-Based Identification of Pathophysiological Processes.- Data Mining Methods in Omics-Based Biomarker Discovery.- Integrated Bioinformatics Analysis for Cancer Target Identification.- Omics-Based Molecular Target and Biomarker Identification.