基于测度传输的密度估计：在生物科学中的应用展望
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences
AI总结：
该研究评估测度传输技术尤其是三角传输映射在生物科学中的应用潜力，发现在有限样本下自适应传输映射密度估计更具优势，可通过统计量揭示隐藏信息，为放射生物学中基因关联及辐射动态的假设生成提供工具。
摘要：
测度传输方法的众多优势之一在于，它能为服从一大类概率测度分布的数据的处理与分析提供统一框架。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（具体而言是三角传输映射的使用）作为服务于生物科学研究的工作流程组成部分的潜力。我们特别关注样本数据量有限的场景，这类场景在放射生物学等领域十分常见。我们发现，在样本数据量有限的情况下估计分布密度函数时，自适应传输映射更具优势。具体来说，通过对可用数据样本的一系列随机子集训练得到的自适应传输映射序列进行计算，收集到的统计量能够揭示数据中隐藏的信息。因此，在本文研究的放射生物学应用中，该方法为生成关于基因关联及其在辐射暴露下动态变化的假设提供了工具。
英文摘要：
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. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
