| An exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems--novel insights into malaria. | |
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MedLine Citation:
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PMID: 21931645 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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®, 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. |
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Authors:
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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:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2011-09-09 |
Journal Detail:
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Title: PloS one Volume: 6 ISSN: 1932-6203 ISO Abbreviation: PLoS ONE Publication Date: 2011 |
Date Detail:
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Created Date: 2011-09-20 Completed Date: 2012-03-01 Revised Date: 2012-04-26 |
Medline Journal Info:
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Nlm Unique ID: 101285081 Medline TA: PLoS One Country: United States |
Other Details:
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Languages: eng Pagination: e24085 Citation Subset: IM |
Affiliation:
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Institut Pasteur, Unité de Pathogénie Virale, Paris, France. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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ABO Blood-Group System
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genetics Algorithms* Child Child, Preschool Data Mining / methods* Female Glucosephosphate Dehydrogenase / genetics Humans Infant Logistic Models Malaria / epidemiology*, genetics, parasitology* Male Multivariate Analysis Mutation Plasmodium falciparum / isolation & purification Plasmodium malariae / isolation & purification Polymorphism, Genetic Prognosis Reproducibility of Results Risk Assessment / methods Risk Factors |
| Chemical | |
Reg. No./Substance:
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0/ABO Blood-Group System; EC 1.1.1.49/Glucosephosphate Dehydrogenase |
| Comments/Corrections | |
Erratum In:
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PLoS One. 2011;6(10). doi:10.1371/annotation/654e34ce-f1cd-4207-b2ac-ebc873b821e9 |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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