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A MapReduce Approach for Ridge Regression in Neuroimaging-Genetic Studies

Abstract : In order to understand the large between-subject variability observed in brain organization and assess factor risks of brain diseases, 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 high-dimensional and complex data is carried out with increasingly sophisticated techniques and represents a great computational challenge. To be fully exploited, the concurrent increase of computational power then requires designing new parallel algorithms. The MapReduce framework coupled with efficient algorithms permits to deliver a scalable analysis tool that deals with high-dimensional data and hundreds of permutations in a few hours. On a real functional MRI dataset, this tool shows promising results.
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Submitted on : Monday, September 10, 2012 - 10:00:01 AM
Last modification on : Friday, November 18, 2022 - 9:23:08 AM
Long-term archiving on: : Tuesday, December 11, 2012 - 3:38:58 AM


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  • HAL Id : hal-00730385, version 1



Benoit da Mota, Michael Eickenberg, Soizic Laguitton, Vincent Frouin, Gaël Varoquaux, et al.. A MapReduce Approach for Ridge Regression in Neuroimaging-Genetic Studies. DCICTIA-MICCAI - Data- and Compute-Intensive Clinical and Translational Imaging Applications in conjonction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention - 2012, Oct 2012, Nice, France. ⟨hal-00730385⟩



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