Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships - Chia-Yi Cheng; Ying Li; Kranthi Varala; Jessica Bubert; Ji Huang; Grace J. Kim; Justin Halim; Jennifer Arp; Hung-Jui S. Shih; Grace Levinson; Seo Hyun Park; Ha Young Cho; et al. (Nature Communications, 2021)
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.