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Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

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 two weeks deployment on hundreds of virtual machines.
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Submitted on : Friday, August 22, 2014 - 11:37:40 AM
Last modification on : Monday, December 13, 2021 - 9:16:03 AM
Long-term archiving on: : Thursday, November 27, 2014 - 1:41:03 PM


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Benoit da Mota, Radu Tudoran, Alexandru Costan, Gaël Varoquaux, Goetz Brasche, et al.. Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud. Frontiers in Neuroinformatics, Frontiers, 2014, Recent advances and the future generation of neuroinformatics infrastructure, 8, ⟨10.3389/fninf.2014.00031⟩. ⟨hal-01057325⟩



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