Proteomizer: Leveraging the Transcriptome-Proteome Mismatch to Infer Novel Gene Regulatory Relations.

ABSTRACT: The correlation between transcriptomic (Tx) and proteomic (Px) profiles remains modest, typically around r=0.5 across genes and r=0.3 across samples, limiting the utility of transcriptomic data as a proxy for protein abundance. To address this, we introduce Proteomizer, a deep learning platform designed to infer a sample's Px landscape from its Tx and miRNomic (Mx) profiles. Trained on 8,613 matched Tx-Mx-Px samples from TCGA and CPTAC, Proteomizer achieved a Tx-Px correlation of r=0.68 , representing the highest performance reported to date for this task. We further developed a Monte Carlo simulation framework to evaluate the impact of proteomization on differential expression analysis. Proteomizer substantially improved the accuracy of differential gene expression detection, with p-value

SUBMITTER: Deangeli G 

PROVIDER: S-EPMC12262288 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature