Large-scale labeling and assessment of sex bias in publicly available expression data.

ABSTRACT:

Background

Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray an

SUBMITTER: Flynn E 

PROVIDER: S-EPMC8011224 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature