Guzzi P.H. (Ed.) Microarray Data Analysis. Methods and Applications

2nd Ed. — Humana Press, 2016 — 226p. — (Methods in Molecular Biology 1375) — ISBN: 978-1-4939-3173-6 (eBook), 978-1-4939-3172-9 (Hardcover).This volume covers a large area, from the description of methodologies for data analysis to the real application. Chapters focus on methodologies for preprocessing of microarray data, a survey of miRNA Data analysis, Cloud-based approaches, application of data mining techniques for data analysis, biclustering to query different datasets, web-based tool to analyze the evolution of miRNA clusters, application of biclustering to mine patterns of co-regulated genes ontologies, microarray and proteomic Data, Gene Regulatory Network Inference, Gene Regulatory Network methods, analysis of Mouse data for metabolomics studies, analysis of microRNA data in Multiple Myeloma, microarray data analysis in Gliobastomas, and microRNA data in Cardiogenesis.Written for the Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and practical, Microarray Data Analysis: Methods and Applications, Second Edition aims to ensure successful results in the further study of this vital field.Normalization of Affymetrix miRNA Microarrays for the Analysis of Cancer Samples
Methods and Techniques for miRNA Data Analysis
Bioinformatics and Microarray Data Analysis on the Cloud
Classification and Clustering on Microarray Data for Gene Functional Prediction Using R
Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering
MetaMirClust: Discovery and Exploration of Evolutionarily Conserved miRNA Clusters
Analysis of Gene Expression Patterns Using Biclustering
Using Semantic Similarities and csbl.go for Analyzing Microarray Data
Ontology-Based Analysis of Microarray Data
Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency
Integrating Microarray Data and GRNs
Biological Network Inference from Microarray Data, Current Solutions, and Assessments
A Protocol to Collect Specific Mouse Skeletal Muscles for Metabolomics Studies
Functional Analysis of microRNA in Multiple Myeloma
Microarray Analysis in Glioblastomas
Analysis of microRNA Microarrays in Cardiogenesis
Erratum to: Classification and Clustering on Microarray Data for Gene Functional Prediction Using R