SBIR 136 - PREDICTIVE: Knowledge Graphs for Infectious Diseases

NIH RePORTER · NIH · N43 · $298,262 · view on reporter.nih.gov ↗

Abstract

Addressing this critical challenge of drug discovery for infectious disease caused by known of emerging pathogens can be catalyzed by developing structured knowledge about both biological mechanisms underlying the emergence and progression of ID and known drugs with established or hypothesized mechanism of action. This structured knowledge base can support the discovery of novel treatments. Using modern knowledge mining technologies, all available ID-relevant, including biomedical, chemogenomic, and clinical, data can be integrated and organized into a knowledge graph (KG)(1), which can be mined to reveal functional biological pathways involving ID-relevant targets as well as map relevant chemical space of bioactive compounds that can modulate ID targets. In this pilot project. using modern knowledge mining technologies, we will integrate and organize these diverse data into an Infectious Disease KG (IDKG), which can be mined to reveal functional biological pathways underlying the emergence and progression of ID.

Key facts

NIH application ID
11214917
Project number
75N93024C00038-0-9999-1
Recipient
PREDICTIVE, LLC
Principal Investigator
ALEX TROPSHA
Activity code
N43
Funding institute
NIH
Fiscal year
2024
Award amount
$298,262
Award type
Project period
2024-09-05 → 2025-09-04