OTTM

OTTM prioritizes candidate proteins and genes from omics datasets by integrating drug databases and literature mining to identify targets with existing pharmacological interventions.

Key Features:

• Automated Classification: Integrates omics data with literature mining to narrow candidate proteins or genes for further investigation, focusing on those associated with existing drugs or active compounds.
• Drug Availability Assessment: Prioritizes candidates with potential therapeutic relevance by assessing availability of FDA-approved drugs and clinical-trial compounds for identified proteins.
• Literature Mining Integration: Identifies previously reported associations between candidate genes/proteins and diseases from scientific literature to enhance target selection accuracy.

Scientific Applications:

• Proteomics study: Applied to a proteomics study of 4,489 candidate proteins, recommending 40 FDA-approved or clinical-trial drugs and identifying tafenoquine succinate and branaplam as potent inhibitors of Hep-G2 cell viability, suggesting CYC1 and SMN1 as potential therapeutic targets for hepatocellular carcinoma.

Methodology:

Data integration: combines omics datasets with drug databases and scientific literature; Target prioritization: uses automated algorithms to prioritize proteins or genes based on drug availability and disease relevance; Validation recommendations: suggests candidates for further experimental validation.