Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

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 i...

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Detalles Bibliográficos
Autores principales: Cheng, Chia-Yi, Li, Ying, Varala, Kranthi, Bubert, Jessica, Huang, Ji, Kim, Grace J., Halim, Justin, Arp, Jennifer, Shih, Hung-Jui S., Levinson, Grace, Park, Seo Hyun, Cho, Ha Young, Moose, Stephen P., Coruzzi, Gloria M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463701/
https://www.ncbi.nlm.nih.gov/pubmed/34561450
http://dx.doi.org/10.1038/s41467-021-25893-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463701/
https://www.ncbi.nlm.nih.gov/pubmed/34561450
http://dx.doi.org/10.1038/s41467-021-25893-w

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