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.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-00730385
Contributor : Benoit da Mota <>
Submitted on : Monday, September 10, 2012 - 10:00:01 AM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Long-term archiving on : Tuesday, December 11, 2012 - 3:38:58 AM

File

article.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00730385, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

888

Files downloads

781