Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data - Biological Adaptation and Ageing Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

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 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.
Fichier principal
Vignette du fichier
otml_matching_expression_data_v2.pdf (2.96 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

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

Identifiants

Citer

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⟩
482 Consultations
240 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More