Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relation...
Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict pheno...
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
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TN_cdi_doaj_primary_oai_doaj_org_article_df8c1a08be1a4c64bc07660726b1c1e2
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_df8c1a08be1a4c64bc07660726b1c1e2
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-021-25893-w
