Loucoubar C. auteur aut IRD Paul R. auteur aut IRD Bar-Hen A. auteur aut IRD Huret A. auteur aut IRD Tall A. auteur aut IRD Sokhna Cheikh auteur aut IRD Trape Jean-François auteur aut IRD Ly A. B. auteur aut IRD Faye J. auteur aut IRD Badiane A. auteur aut IRD Diakhaby G. auteur aut IRD Sarr F. D. auteur aut IRD Diop A. auteur aut IRD Sakuntabhai A. auteur aut IRD Bureau J. F. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCube (R), to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992-2003, aged 1-5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection <= 10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCube (R) rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCube (R) efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the complexity inherent in biological systems. specialized 052 020 6 9 e24085 2011 1932-6203 https://www.documentation.ird.fr/hor/fdi:010053838 10.1371/journal.pone.0024085 1932-6203 [F B010053838] https://www.documentation.ird.fr/hor/fdi:010053838 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-02/010053838.pdf IRD - Base Horizon / Pleins textes 2011-11-07 2019-03-27 fdi:010053838 fre