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Article Dans Une Revue Journal of the Royal Statistical Society: Series C Applied Statistics Année : 2024

Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data

Résumé

We present several algorithms designed to learn a pattern of correspondence between 2 data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, the challenge is to better understand micro-RNA regulation in the striatum of Huntington’s disease model mice. The algorithms unfold in 2 stages. First, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn–Knopp algorithm and a mini-batch gradient descent. Second, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. The real data application further illustrates their applicability and interest.

Dates et versions

hal-04626866 , version 1 (27-06-2024)

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Citer

Thi Thanh Yen Nguyen, Warith Harchaoui, Lucile Mégret, Cloé Mendoza, Olivier Bouaziz, et al.. Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data. Journal of the Royal Statistical Society: Series C Applied Statistics, 2024, 73 (3), pp.639-657. ⟨10.1093/jrsssc/qlae005⟩. ⟨hal-04626866⟩

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