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Title: An exhaustive, non-euclidean, non-parametric data mining tool for Unraveling the complexity of biological systems - novel insights into malaria
Authors: Cheikh Loucoubar
Richard Paul
Avner Bar-Hen
Augustin Huret
Adama Tall
Cheikh Sokhna
Jean François Trape
Alioune Badara Ly
Joseph Faye
Abdoulaye Badiane
Gaoussou Diakhaby
Fatoumata Diène Sarr
Aliou Diop
Anavaj Sakuntabhai
Jean François Bureau
Institut Pasteur, Paris
Universite Paris Descartes
Institut Pasteur de Dakar
Ecole des hautes etudes en sante publique
Institute of Health and Science
Institut de Recherche pour le Developpement Dakar
Mahidol University
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology;Medicine
Issue Date: 9-Sep-2011
Citation: PLoS ONE. Vol.6, No.9 (2011)
Abstract: 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®, 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® 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® 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. © 2011 Loucoubar et al.
ISSN: 19326203
Appears in Collections:Scopus 2011-2015

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