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

Author name not available (Why is that?)

Publication date: 21 July 2021

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.

Has companion code repository: https://github.com/yen-nguyen-thi-thanh/wtot_coclust_match

This page was built for publication: Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6373580)