Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Area of Science:

• Computational biology
• Bioinformatics
• Genomics

Background:

• NIH-funded projects Library of Integrated Network-based Cellular Signatures (LINCS) and Illuminating the Druggable Genome (IDG) provide rich datasets for disease research.
• LINCS offers omics data, while IDG provides curated knowledge for drug target discovery.
• These resources are crucial for understanding complex diseases like Parkinson's disease (PD).

Purpose of the Study:

• To develop a novel approach for identifying and prioritizing drug targets for complex diseases.
• To integrate LINCS and IDG data using a Knowledge Graph Analytics Platform (KGAP).
• To apply KGAP to discover and validate new drug targets for Parkinson's disease.

Main Methods:

• Integrated LINCS and IDG data into the Knowledge Graph Analytics Platform (KGAP).
• Utilized graph database and analytical methods with strong semantics for interpretability.
• Employed DrugCentral and TIN-X resources for target identification, prioritization, and validation against known PD genes.

Main Results:

• KGAP successfully identified and prioritized drug target hypotheses for diseases, exemplified by Parkinson's disease.
• The KG-analytic scoring function was validated against a gold standard dataset.
• SYNGR3 (Synaptogyrin-3) was identified as a plausible novel drug target for PD through case study investigation.

Conclusions:

• The synergy of LINCS and IDG through KGAP enhances graph analytics for investigating complex diseases.
• KGAP facilitates the identification and prioritization of novel drug targets.
• The KGAP approach is broadly applicable to various disease areas beyond PD.