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Time-dependent gaussian process regression and significance analysis for sparse time-series

Markus Heinonen 1, 2, * Olivier Guipaud 3 Fabien Milliat 3 Béatrice Micheau 4 Valérie Buard 3 Florence d'Alché-Buc 2, 1 
* Corresponding author
2 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
Abstract : Gaussian process regression (GPR) has been extensively used for modelling and differential testing of biological time-series measurements due to its robustness and interpretability. However, the standard gaussian process assumes stationary model dynamics and is a poor fit for common perturbation experiments, where we expect to see rapid changes after the perturbation and diminishing rate of state change as the cell returns back to a stable state. A common application of time-series measurements is the testing of significant difference between two time-serie profiles. The currently used two-sample differential tests, based on gaussian processes, focus on comparing model likelihoods over a subset of measured time-points, and hence necessitate dense measurements to cover the time axis. We address these problems by proposing time-dependent extensions to both gaussian process regression and significance analysis between time-series. We propose a time-dependent noise model and time-dependent covariance priors, suitable for perturbation experiments. We utilise a novel model inference criteria for sparse measurements, which results in more informative models along time. We propose two novel differential tests for time-series, that both allow significance testing at non-observed time-points. We apply the extended GPR model for analysis of differential expression of irradiated human umbilical vein endothelial cell (HUVEC) transcriptomics dataset.
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Contributor : Florence D'Alché-Buc Connect in order to contact the contributor
Submitted on : Monday, July 15, 2013 - 12:02:19 PM
Last modification on : Sunday, June 26, 2022 - 11:59:20 AM


  • HAL Id : hal-00844474, version 1


Markus Heinonen, Olivier Guipaud, Fabien Milliat, Béatrice Micheau, Valérie Buard, et al.. Time-dependent gaussian process regression and significance analysis for sparse time-series. Seventh international workshop on Machine Learning in Systems Biology, satellite meeting of ISMB'2013, Jul 2013, Berlin, Germany. ⟨hal-00844474⟩



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