Omics technologies have the potential to facilitate the discovery of new biomarkers. However, only few omics-derived biomarkers have been successfully translated into clinical applications to date. Feature selection is a crucial step in this process that identifies small sets of features with high predictive power. Models consisting of a limited number of features are not only more robust in analytical terms, but also ensure cost-effectiveness and clinical translatability of new biomarker panels. Here we introduce GARBO, a novel multi-island adaptive genetic algorithm to simultaneously optimize accuracy and set size in omics-driven biomarker discovery problems.

Feature set optimization in biomarker discovery from genome scale data / Fortino, V; Scala, G; Greco, D. - In: BIOINFORMATICS. - ISSN 1367-4803. - 36:11(2020), pp. 3393-3400. [10.1093/bioinformatics/btaa144]

Feature set optimization in biomarker discovery from genome scale data

Scala, G;
2020

Abstract

Omics technologies have the potential to facilitate the discovery of new biomarkers. However, only few omics-derived biomarkers have been successfully translated into clinical applications to date. Feature selection is a crucial step in this process that identifies small sets of features with high predictive power. Models consisting of a limited number of features are not only more robust in analytical terms, but also ensure cost-effectiveness and clinical translatability of new biomarker panels. Here we introduce GARBO, a novel multi-island adaptive genetic algorithm to simultaneously optimize accuracy and set size in omics-driven biomarker discovery problems.
2020
Feature set optimization in biomarker discovery from genome scale data / Fortino, V; Scala, G; Greco, D. - In: BIOINFORMATICS. - ISSN 1367-4803. - 36:11(2020), pp. 3393-3400. [10.1093/bioinformatics/btaa144]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/795583
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