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Microarray Data Analysis

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Table of Contents

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    Book Overview
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    Chapter 236 Bioinformatics and Microarray Data Analysis on the Cloud.
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    Chapter 237 MetaMirClust: Discovery and Exploration of Evolutionarily Conserved miRNA Clusters.
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    Chapter 238 Methods and Techniques for miRNA Data Analysis.
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    Chapter 239 Normalization of Affymetrix miRNA Microarrays for the Analysis of Cancer Samples.
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    Chapter 240 Classification and Clustering on Microarray Data for Gene Functional Prediction Using R
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    Chapter 241 Using Semantic Similarities and csbl.go for Analyzing Microarray Data
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    Chapter 242 Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency
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    Chapter 245 Microarray Analysis in Glioblastomas
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    Chapter 246 Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering
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    Chapter 247 Analysis of microRNA Microarrays in Cardiogenesis.
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    Chapter 248 A Protocol to Collect Specific Mouse Skeletal Muscles for Metabolomics Studies
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    Chapter 249 Ontology-Based Analysis of Microarray Data
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    Chapter 250 Functional Analysis of microRNA in Multiple Myeloma.
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    Chapter 252 Integrating Microarray Data and GRNs.
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    Chapter 256 Erratum to: Classification and Clustering on Microarray Data for Gene Functional Prediction Using R
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    Chapter 280 Analysis of Gene Expression Patterns Using Biclustering
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    Chapter 284 Biological Network Inference from Microarray Data, Current Solutions, and Assessments.
Attention for Chapter 249: Ontology-Based Analysis of Microarray Data
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Chapter title
Ontology-Based Analysis of Microarray Data
Chapter number 249
Book title
Microarray Data Analysis
Published in
Methods in molecular biology, May 2015
DOI 10.1007/7651_2015_249
Pubmed ID
Book ISBNs
978-1-4939-3172-9, 978-1-4939-3173-6
Authors

Agapito Giuseppe, Marianna Milano, Giuseppe, Agapito, Milano, Marianna

Abstract

The importance of semantic-based methods and algorithms for the analysis and management of biological data is growing for two main reasons. From a biological side, knowledge contained in ontologies is more and more accurate and complete, from a computational side, recent algorithms are using in a valuable way such knowledge. Here we focus on semantic-based management and analysis of protein interaction networks referring to all the approaches of analysis of protein-protein interaction data that uses knowledge encoded into biological ontologies.Semantic approaches for studying high-throughput data have been largely used in the past to mine genomic and expression data. Recently, the emergence of network approaches for investigating molecular machineries has stimulated in a parallel way the introduction of semantic-based techniques for analysis and management of network data. The application of these computational approaches to the study of microarray data can broad the application scenario of them and simultaneously can help the understanding of disease development and progress.

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The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%