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Multiple-input multiple-output causal strategies for gene selection.

par Bontempi, Gianluca ;Haibe-Kains, Benjamin ;Desmedt, Christine ;Sotiriou, Christos ;Quackenbush, John
Référence BMC bioinformatics, 12, page (458)
Publication Publié, 2011

Article révisé par les pairs

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Résumé :

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.