Machine learning methodologies can be applied readily to biological problems, but standard training and testing methods are not designed to control for evolutionary relatedness or other biological phenomena. In their paper recently published in PNAS, Drs. Washburn and Wang propose, implement, and test two methods to control for and utilize evolutionary relatedness within a predictive deep learning framework. The methods are tested and applied within the context of predicting mRNA expression levels from whole-genome DNA sequence data and are applicable across biological organisms. Potential use cases for the methods include plant and animal breeding, disease research, gene editing, and others.
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