Document Detail

An exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems--novel insights into malaria.
MedLine Citation:
PMID:  21931645     Owner:  NLM     Status:  MEDLINE    
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.
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
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2011-09-09
Journal Detail:
Title:  PloS one     Volume:  6     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2011  
Date Detail:
Created Date:  2011-09-20     Completed Date:  2012-03-01     Revised Date:  2013-06-27    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e24085     Citation Subset:  IM    
Institut Pasteur, Unité de Pathogénie Virale, Paris, France.
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MeSH Terms
ABO Blood-Group System / genetics
Child, Preschool
Data Mining / methods*
Glucosephosphate Dehydrogenase / genetics
Logistic Models
Malaria / epidemiology*,  genetics,  parasitology*
Multivariate Analysis
Plasmodium falciparum / isolation & purification
Plasmodium malariae / isolation & purification
Polymorphism, Genetic
Reproducibility of Results
Risk Assessment / methods
Risk Factors
Reg. No./Substance:
0/ABO Blood-Group System; EC Dehydrogenase
Erratum In:
PLoS One. 2011;6(10). doi:10.1371/annotation/654e34ce-f1cd-4207-b2ac-ebc873b821e9

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