Deep Learning Enables Fast and Accurate Imputation of Gene Expression.

ABSTRACT: A question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we propose two novel deep learning methods, PMI and GAIN-GTEx, for gene expression imputation. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We show that our approaches compare favorably to several standard and state-of-the-art imputation methods in terms of predictive performance and runtime in two case studies and two imputation scenarios. In comparison conducted on the prote

SUBMITTER: Vinas R 

PROVIDER: S-EPMC8076954 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature