An exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems : novel insights into malaria
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
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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
Plos One
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