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Poster Communications Year : 2015

High-dimensional longitudinal genomic data: a survey and evaluation of publicly available implementations of machine learning methods

Abstract

Problems related to high–dimensionality arise nowadays in many fields of biomedical and clinical trials research, in which longitudinal studies are usually conducted. In these fields, high–dimensional data have lead to the publication of an increasing number of related articles. However, methods appropriate for high-dimensional data analysis, accounting simultaneously for the longitudinal dimension of the data, have been proposed only recently. We performed a review of articles proposing these appropriate methods when assuming a mixed effects model. We evaluated by simulations those methods that are implemented through publicly available codes. L1 regularization methods were the most common approaches. We discuss capacities and limitations with a view to analyzing the DALIA-1 trial data, a therapeutic HIV vaccine clinical trial in which 19 patients were vaccinated. This trial evaluated the administration of a dendritic cell based vaccine to HIV infected patients as a way to boost their immune response against HIV infection. A huge number of data were collected : longitudinal gene expression in the blood was repeatedly measured with microarrays over the course of the trial, as well as blood cell markers that were measured with flow cytometry and multiplex technologies.
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Dates and versions

hal-01253151 , version 1 (08-01-2016)

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  • HAL Id : hal-01253151 , version 1

Cite

Perrine Soret, Marta Fernandez Avalos, Rodolphe Thiébaut. High-dimensional longitudinal genomic data: a survey and evaluation of publicly available implementations of machine learning methods. Statistical Analysis of Massive Genomic Data, Nov 2015, Evry, France. , 2015. ⟨hal-01253151⟩

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INRIA INRIA2 U1219
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