Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

Participants : Virgile Fritsch, Bertrand Thirion, Gaël Varoquaux.

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.

Figure 6. Overview of the multi site deployment of a hierarchical Tomus-MapReduce compute engine. 1) The end-user uploads the data and configures the statistical inference procedure on a webpage. 2) The Splitter partitions the data and manages the workload. The compute engines retrieves job information trough the Windows Azure Queues. 3) Compute engines perform the map and reduce jobs. The management deployment is informed of the progression via the Windows Azure Queues system and thus can manage the execution of the global reducer. 4) The user downloads the results of the computation on the webpage of the experiment.

More details can be found in [17] .