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Machine learning patterns for neuroimaging-genetic studies in the cloud.
- Source :
- Frontiers in Neuroinformatics; Apr2014, p1-9, 9p
- Publication Year :
- 2014
-
Abstract
- Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16625196
- Database :
- Complementary Index
- Journal :
- Frontiers in Neuroinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 95956159
- Full Text :
- https://doi.org/10.3389/fninf.2014.00031