Microarray Data Analysis -

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Résumé :
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.

Sommaire:
Di Wu and Michael P. Gantier?2) Methods and Techniques for miRNA Data AnalysisFrancesca Cristiano and Pierangelo Veltri ?3) Bioinformatics and Microarray Data Analysis on The CloudBarbara Calabrese and Mario Cannataro?4) Classification and Clustering on Microarray Data for Gene Functional Prediction using RLiliana L?pez Kleine, Rosa Monta?o, and Francisco Torres-Avil?s?5) Querying co-regulated genes on diverse gene expression datasets via biclusteringMehmet Deveci, Onur Kucuktunc, Kernal Eren, Doruk Bozadag, Kamer Kaya, and Umit V. Catalyurek??6) MetaMirClust: Discovery and Exploration of Evolutionarily Conserved miRNA ClustersWen-Ching Chan and Wen-chang Lin?7) Analysis of Gene Expression Patterns using BiclusteringSwarup Roy, Dhruba K Bhattacharyya, and Jugal K Kalita?8) Using semantic Similarities and csbl.go for Analyzing Microarray DataKristian Ovaska?9) Ontology Based Analysis of Microarray DataGiuseppe Agapito and Marianna Milano ?10) Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency? Jiangxin Wang, Gang Wu, Lei Chen, and Weiwen Zhang ?11) Integrating Microarray Data and GRNsL. Koumakis, G. Potamias, M. Tsiknakis, M. Zervakis, and V. Moustakis?12) Biological Network Inference from Microarray Data, Current Solutions and AssessmentsSwarup Roy and Pietro Hiram Guzzi?13) A Protocol to Collect Specific Mouse Skeletal Muscles for Metabolomics StudiesZhuohui Gan, ?Zhenxing Fu, Jennifer C. Stowe, Frank L. Powell, and Andrew D. McCulloch?14) Functional Analysis of microRNA in Multiple MyelomaMaria Teresa Di Martino, Nicola Amodio, Pierfrancesco Tassone, and Pierosandro Tagliaferri?15) Microarray Analysis in GlioblastomasKaumudi M. Bhawe and Manish K. Aghi ?16) Analysis of microRNA Microarrays in CardiogenesisDiego Franco, Fernando Bonet, Francisco Hernandez-Torres, Estefania Lozano-Velasco, Francisco Esteban, and Amelia E Aranega?