One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scarce data scenarios, which are common in domains such as radiation biology, are of particular interest. We find that when data is scarce, sparse transport maps are advantageous. In particular, statistics gathered from computing series of (sparse) adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

翻译：测度传输方法的诸多优势之一在于，它们为按照广泛概率测度分布的数据处理与分析提供了统一的框架。在此背景下，我们展示了计算研究的结果，旨在评估测度传输技术（特别是三角传输映射的使用）作为支持生物科学研究工作流程一部分的潜力。数据稀缺场景（如辐射生物学领域中常见的情形）尤为引人关注。我们发现，当数据稀缺时，稀疏传输映射更具优势。特别地，通过对一系列（稀疏）自适应传输映射（这些映射基于可用数据样本中随机选取的子集进行训练）进行计算所获得的统计数据，能够揭示数据中隐藏的信息。因此，在本文所考虑的辐射生物学应用中，这种方法为生成关于基因关系及其在辐射暴露下动态变化的假设提供了工具。