Microarray Data Analysis: Methods and Applications

by Pietro Hiram Guzzi (Editor)

Overview

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 prools, 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.

Product Details

ISBN-13:	9781493979936
Publisher:	Springer New York
Publication date:	03/13/2019
Series:	Methods in Molecular Biology , #1375
Edition description:	Softcover reprint of the original 2nd ed. 2016
Pages:	226
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of Contents

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 Prool 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.