Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data - Biologie et Pathologie du Neurone Access content directly
Preprints, Working Papers, ... Year : 2023

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

Abstract

We present several algorithms designed to learn a pattern of correspondence between two 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 two 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.
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Dates and versions

hal-03293786 , version 1 (21-07-2021)
hal-03293786 , version 2 (10-01-2022)
hal-03293786 , version 3 (01-03-2023)

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Thi Thanh Yen Nguyen, Warith Harchaoui, Lucile Mégret, Cloe 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. 2023. ⟨hal-03293786v3⟩
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