Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data
提出几种算法，通过最优传输和仿射变换学习两个数据集间的对应模式，用于匹配符合已知参数模型的元素，并在亨廷顿病小鼠模型的micro-RNA调控数据中验证了其有效性。
Abstract 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.