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

摘要

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 predictive 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 classifiers mainly involving RF and ANOVA. The visualization of predictive biomarkers is based on heatmaps while FCA is mainly used for visualization and interpretation purposes, complementing the computational power of numerical methods.