Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies.

ABSTRACT: Therapeutic target identification is challenging in drug discovery, particularly for rare and orphan diseases. Here, we propose a disease signature, TRESOR, which characterizes the functional mechanisms of each disease through genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data, and develop machine learning methods for predicting inhibitory and activatory therapeutic targets for various diseases from target perturbation signatures (i.e., gene knockdown and overexpression). TRESOR enables highly accurate identification of target candidate proteins that counteract disease-specific transcriptome patterns, and the Bayesian optimization with omics-based disease similarities achieves the performance enhancement for diseases with few or no known targets. We m

SUBMITTER: Namba S 

PROVIDER: S-EPMC12008218 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature