Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

ABSTRACT: Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome

SUBMITTER: Tasaki S 

PROVIDER: S-EPMC8814036 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature