Knowledge Graph Analytics Platform with LINCS and IDG for Parkinson's Disease Target Illumination
NIH programs LINCS, “Library of Integrated Network-based Cellular Signatures”, and IDG, “Illuminating the Druggable Genome”, have generated rich open access datasets for the study of the molecular basis of health and disease. LINCS provides experimental genomic and transcriptomic evidence. IDG provides curated knowledge for illumination and prioritization of novel protein drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases. Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) for identification and prioritization of drug target hypotheses, via open source package kgap_lincs-idg. We investigated results for Parkinson’s Disease (PD), which inflicts severe harm on human health and resists traditional approaches. Approved drug indications from IDG’s DrugCentral were starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as targets by integration with IDG. The KGAP scoring function was validated against genes associated with PD with published mechanism-of-action gold standard elucidation. IDG was used to rank and filter KGAP results for novel PD targets, and manual validation. KGAP thereby empowers the identification and prioritization of novel drug targets, for complex diseases such as PD.
Yang, Jeremy; Chris Gessner; Joel Duerksen; Daniel Biber; Jessica Binder; Murat Ozturk; Brian Foote; Robin McEntire; Kyle Stirling; Ying Ding; and David Wild. "Knowledge Graph Analytics Platform with LINCS and IDG for Parkinson's Disease Target Illumination." (2022). https://digitalrepository.unm.edu/hsc-bbhrd/52
Poster presented at the Brain & Behavioral Health Research Day 2022