Network & Data-driven Approaches

Solutions for Your Data to Accelerate Therapeutic R&D

bioinformatics

Target Identification

  • Identify underlying molecular characteristics and signature of genes linked to disease state.
  • Learn from diverse data: multi-omics and/or clinical data to identify new pathways and markers that lead to new therapeutic targets.
  • Assess druggability of targets by learning from existing and failed drug targets structure and property data.

Pathway Analysis

  • Use pathways data sources (GO, KEGG, Panther, MSigDB etc.) to uncover MOA and enriched terms.
  • Networks to identify combination therapies. 

Biomarker Analysis

  • Predict patient's response to drug to distinguish responders and non-responders.
  • Machine learning for clinical trial patient selection and stratification/classification.

Biomarker analysis example


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AI & QSP

Identifying parameter regimes for virtual patients, combining network-based reasoning with quantitative models of biology.

Drug Repurposing

Network analysis and ML/AI approaches on integrated biological, pharmalogical and clinical data to identify new therapeutic uses for a drug or target.

bioinformatics