Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

ABSTRACT: Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family-guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two a

SUBMITTER: Washburn JD 

PROVIDER: S-EPMC6431157 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature