Title |
Machine learning patterns for neuroimaging-genetic studies in the cloud
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Published in |
Frontiers in Neuroinformatics, April 2014
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DOI | 10.3389/fninf.2014.00031 |
Pubmed ID | |
Authors |
Benoit Da Mota, Radu Tudoran, Alexandru Costan, Gaël Varoquaux, Goetz Brasche, Patricia Conrod, Herve Lemaitre, Tomas Paus, Marcella Rietschel, Vincent Frouin, Jean-Baptiste Poline, Gabriel Antoniu, Bertrand Thirion |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Brazil | 2 | 4% |
Malaysia | 1 | 2% |
Germany | 1 | 2% |
France | 1 | 2% |
Unknown | 51 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 14 | 25% |
Student > Bachelor | 6 | 11% |
Researcher | 6 | 11% |
Professor | 5 | 9% |
Professor > Associate Professor | 3 | 5% |
Other | 8 | 14% |
Unknown | 14 | 25% |
Readers by discipline | Count | As % |
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Computer Science | 12 | 21% |
Agricultural and Biological Sciences | 7 | 13% |
Neuroscience | 6 | 11% |
Engineering | 5 | 9% |
Psychology | 2 | 4% |
Other | 7 | 13% |
Unknown | 17 | 30% |