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.
Complete list of metadatas

Cited literature [35 references]  Display  Hide  Download

https://hal.inria.fr/hal-01057325
Contributor : Bertrand Thirion <>
Submitted on : Friday, August 22, 2014 - 11:37:40 AM
Last modification on : Friday, June 28, 2019 - 3:02:50 PM
Long-term archiving on : Thursday, November 27, 2014 - 1:41:03 PM

File

frontiers.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

1523

Files downloads

571