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Microarray Data Analysis

Overview of attention for book
Cover of 'Microarray Data Analysis'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 236 Bioinformatics and Microarray Data Analysis on the Cloud.
  3. Altmetric Badge
    Chapter 237 MetaMirClust: Discovery and Exploration of Evolutionarily Conserved miRNA Clusters.
  4. Altmetric Badge
    Chapter 238 Methods and Techniques for miRNA Data Analysis.
  5. Altmetric Badge
    Chapter 239 Normalization of Affymetrix miRNA Microarrays for the Analysis of Cancer Samples.
  6. Altmetric Badge
    Chapter 240 Classification and Clustering on Microarray Data for Gene Functional Prediction Using R
  7. Altmetric Badge
    Chapter 241 Using Semantic Similarities and csbl.go for Analyzing Microarray Data
  8. Altmetric Badge
    Chapter 242 Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency
  9. Altmetric Badge
    Chapter 245 Microarray Analysis in Glioblastomas
  10. Altmetric Badge
    Chapter 246 Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering
  11. Altmetric Badge
    Chapter 247 Analysis of microRNA Microarrays in Cardiogenesis.
  12. Altmetric Badge
    Chapter 248 A Protocol to Collect Specific Mouse Skeletal Muscles for Metabolomics Studies
  13. Altmetric Badge
    Chapter 249 Ontology-Based Analysis of Microarray Data
  14. Altmetric Badge
    Chapter 250 Functional Analysis of microRNA in Multiple Myeloma.
  15. Altmetric Badge
    Chapter 252 Integrating Microarray Data and GRNs.
  16. Altmetric Badge
    Chapter 256 Erratum to: Classification and Clustering on Microarray Data for Gene Functional Prediction Using R
  17. Altmetric Badge
    Chapter 280 Analysis of Gene Expression Patterns Using Biclustering
  18. Altmetric Badge
    Chapter 284 Biological Network Inference from Microarray Data, Current Solutions, and Assessments.
Attention for Chapter 236: Bioinformatics and Microarray Data Analysis on the Cloud.
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Chapter title
Bioinformatics and Microarray Data Analysis on the Cloud.
Chapter number 236
Book title
Microarray Data Analysis
Published in
Methods in molecular biology, April 2015
DOI 10.1007/7651_2015_236
Pubmed ID
25863787
Book ISBNs
978-1-4939-3172-9, 978-1-4939-3173-6
Authors

Calabrese, Barbara, Cannataro, Mario, Barbara Calabrese, Mario Cannataro

Abstract

High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
Unknown 20 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Ph. D. Student 3 14%
Student > Master 3 14%
Student > Bachelor 3 14%
Unspecified 2 9%
Other 3 14%
Unknown 4 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 23%
Computer Science 4 18%
Unspecified 2 9%
Biochemistry, Genetics and Molecular Biology 1 5%
Social Sciences 1 5%
Other 2 9%
Unknown 7 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2015.
All research outputs
#15,330,127
of 22,800,560 outputs
Outputs from Methods in molecular biology
#5,333
of 13,120 outputs
Outputs of similar age
#157,556
of 264,661 outputs
Outputs of similar age from Methods in molecular biology
#6
of 22 outputs
Altmetric has tracked 22,800,560 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,120 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,661 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.

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