标题： 关于代谢物谱监督排名的最新进展 显示英文标题

标题： On Recent Advances in Supervised Ranking for Metabolite Profiling

摘要： 本文聚焦于代谢组学领域产生的数据，代谢组学是一个迅速发展的研究领域，关注于分析化学指纹（即代谢物谱）。 代谢物谱是由发生在生物细胞、组织或器官中的特定化学过程留下的。 本文的主要目的是开发和实施评分技术，以便按照给定代谢物在某种生物液体中高水平存在的条件概率的大小顺序，对所有可能的代谢物谱进行排序。 在详细描述了需要从中学习决策规则的功能数据之后，本文详细描述了几种基于K-部排名最新进展的预测问题的方法。 随后，这些方法在几个真实数据集上的性能得到了彻底的研究。 显示英文摘要

摘要： This paper focuses on data arising from the field of metabolomics, a rapidly developing area concerned by the analysis of the chemical fingerprints (i.e. the metabolite profile). The metabolite profile is left by specific chemical processes occurring in biological cells, tissues or organs. It is the main purpose of this article to develop and implement scoring techniques so as to rank all possible metabolic profiles by increasing order of magnitude of the conditional probability that a given metabolite is present at high levels in a certain biological fluid. After a detailed description of the (functional) data from which decision rules must be learnt, several approaches to this predictive problem, based on recent advances in K-partite ranking are described at length. Their performance on several real datasets are next thoroughly investigated.