Microarray Data Analysis
This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and...
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| Format: | eBook |
| Language: | English |
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New York, NY
Humana
2022, 2022
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| Edition: | 1st ed. 2022 |
| Series: | Methods in Molecular Biology
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Tools in Pharmacogenomics Biomarker Identification for Cancer Patients
- High Performance Framework to Analyze Microarray Data
- Web and Cloud Computing to Analyze Microarray Data
- A Microarray Analysis Technique Using a Self-Organizing Multi-Agent Approach
- Improving Analysis and Annotation of Microarray Data with Protein Interactions
- Algorithms to Preprocess Microarray Image Data
- Microarray Data Preprocessing: From Experimental Design to Differential Analysis
- Supervised Methods for Biomarker Detection from Microarray Experiments
- Unsupervised Algorithms for Microarray Sample Stratification
- Pathway Enrichment Analysis of Microarray Data
- Network Analysis of Microarray Data
- geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO)
- Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies
- Alignment of Microarray Data
- Integration of DNA Microarray with Clinical and Genomic Data
- ClusteringMethods for Microarray Data Sets
- Microarray Data Analysis Protocol
- Using Gene Ontology to Annotate and Prioritize Microarray Data
- Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma