Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
| Autor: | Ying Li, Grace Kim, Grace Levinson, Gloria M. Coruzzi, Chia Yi Cheng, Hung Jui S. Shih, Jessica Bubert, Kranthi Varala, Ha Young Cho, Ji Huang, Jennifer Arp, Justin Halim, Seo Hyun Park, Stephen P. Moose |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Genotype
Nitrogen Systems biology Science Arabidopsis General Physics and Astronomy Machine learning computer.software_genre Zea mays Article General Biochemistry Genetics and Molecular Biology Evolution Molecular Machine Learning Transcriptome Species Specificity Gene Expression Regulation Plant Feature (machine learning) Transcriptomics Gene Transcription factor Multidisciplinary Models Genetic biology business.industry Genetic Variation Genomics General Chemistry biology.organism_classification Phenotype Predictive power Artificial intelligence business computer Genome Plant |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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 phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine. Predicting complex phenotypes from genomic information is still a challenge. Here, the authors use an evolutionarily informed machine learning approach within and across species to predict genes affecting nitrogen utilization in crops, and show their approach is also useful in mammalian systems. |
| Databáze: | OpenAIRE |
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