  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.
