A Hybrid Knowledge Discovery Approach for Mining Predictive Biomarkers in Metabolomic Data

Dhouha Grissa 1, 2 Blandine Comte 2 Estelle Pujos-Guillot 2 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : The analysis of complex and massive biological data issued from metabolomic analytical platforms is a challenge of high importance. The analyzed datasets are constituted of a limited set of individuals and a large set of features where predictive biomarkers of clinical outcomes should be mined. Accordingly, in this paper, we propose a new hybrid knowledge discovery approach for discovering meaningful predic-tive biological patterns. This hybrid approach combines numerical classifiers such as SVM, Random Forests (RF) and ANOVA, with a symbolic method, namely Formal Concept Analysis (FCA). The related experiments show how we can discover among the best potential predictive biomarkers of metabolic diseases thanks to specific combinations of clas-sifiers mainly involving RF and ANOVA. The visualization of predictive biomarkers is based on heatmaps while FCA is mainly used for visual-ization and interpretation purposes, complementing the computational power of numerical methods.
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Conference papers
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Submitted on : Wednesday, December 21, 2016 - 1:54:52 PM
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Dhouha Grissa, Blandine Comte, Estelle Pujos-Guillot, Amedeo Napoli. A Hybrid Knowledge Discovery Approach for Mining Predictive Biomarkers in Metabolomic Data. ECML PKDD, Sep 2016, Riva del garda, Italy. pp.572 - 587, ⟨10.1007/978-3-319-46128-1_36⟩. ⟨hal-01421011⟩



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