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dc.contributor.authorFortino, V
dc.contributor.authorScala, G
dc.contributor.authorGreco, D
dc.date.accessioned2020-06-15T12:10:27Z
dc.date.available2020-06-15T12:10:27Z
dc.date.issued2020
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/8188
dc.description.abstractMotivation 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. Results Compared to existing methods, GARBO enables the identification of biomarker sets that best optimize the trade-off between classification accuracy and number of biomarkers. We tested GARBO and six alternative selection methods with two high relevant topics in precision medicine: cancer patient stratification and drug sensitivity prediction. We found multivariate biomarker models from different omics data types such as mRNA, miRNA, copy number variation, mutation and DNA methylation. The top performing models were evaluated by using two different strategies: the Pareto-based selection, and the weighted sum between accuracy and set size (w = 0.5). Pareto-based preferences show the ability of the proposed algorithm to search minimal subsets of relevant features that can be used to model accurate random forest-based classification systems. Moreover, GARBO systematically identified, on larger omics data types, such as gene expression and DNA methylation, biomarker panels exhibiting higher classification accuracy or employing a number of features much lower than those discovered with other methods. These results were confirmed on independent datasets.
dc.language.isoenglanti
dc.publisherOxford University Press (OUP)
dc.relation.ispartofseriesBioinformatics
dc.relation.urihttp://dx.doi.org/10.1093/bioinformatics/btaa144
dc.rightsAll rights reserved
dc.titleFeature Set Optimization in Biomarker Discovery From Genome Scale Data
dc.description.versionfinal draft
dc.contributor.departmentSchool of Medicine / Biomedicine
uef.solecris.id68978748en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© The Author(s) 2020
dc.relation.doi10.1093/bioinformatics/btaa144
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3393-3400
dc.relation.issn1367-4803
dc.relation.issue11
dc.relation.volume36
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessEi


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