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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Application of microarray analysis on computer cluster and cloud platforms.

C Bernau¹, A-L Boulesteix, J Knaus

¹Department for Medical Informatics, Biometry and Epidemiology, IBE, Ludwig-Maximilians-University Munich, Munich, Germany. bernau@ibe.med.uni-muenchen.de

Methods of Information in Medicine

|November 29, 2012

Summary

This summary is machine-generated.

Cloud computing offers an efficient alternative for computationally intensive statistical projects, comparable to traditional computer clusters. This approach is suitable for high-dimensional biological data analysis, providing flexibility and scalability.

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Area of Science:

Computational biology
Bioinformatics
Statistical computing

Background:

High-dimensional biological data analysis is computationally intensive.
Common methods like resampling and permutation tests require repeated analyses.
Parallelization is key, leading to the establishment of computer clusters.

Purpose of the Study:

Evaluate the efficiency of cloud resources for statistical projects.
Compare cloud implementations with traditional computer clusters.
Explore combining computer cluster and cloud resources.

Main Methods:

Microarray analysis procedures were used for comparison.
Runtimes were compared across different platforms (computer cluster vs. cloud).
Efficiency of parallelization was assessed on both platforms.

Main Results:

Amazon Web Services offers suitable instance types for statistical projects.
Sufficient network capacity and comparable parallelization efficiency were observed.
Cloud implementations demonstrated efficiency comparable to computer clusters.

Conclusions:

Cloud resources can efficiently support many statistical projects.
Transitioning to cloud computing may significantly alter existing workflows.
Combining cluster and cloud resources presents a viable strategy.