Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

ABSTRACT: Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks^1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study⁷ to evaluate the ability of current methods to predict gene expression variation across ind

SUBMITTER: Sasse A 

PROVIDER: S-EPMC10055057 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature