Causaly

1. Enter disease or target of interest into Causaly's AI copilot.

2. Explore the knowledge graph to identify potential targets and their relationships to the disease.

3. Filter targets based on criteria such as study type, impact factor, and novelty.

4. Analyze supporting evidence to understand directional effects and red flags.

5. Prioritize targets based on the strength of evidence and potential for therapeutic intervention.

1. Ask questions in Causaly’s scientific AI copilot about the disease to identify potential biomarkers.

2. Access hundreds of millions of external and internal data points to generate a transparent landscape view of genes, proteins, and functions implicated in the disease.

3. Navigate through a multi-dimensional web of text and visuals to explore the knowledge landscape of genes, proteins, cellular, and molecular functions.

4. Evaluate the strength of evidence for each biomarker candidate.

5. Validate potential biomarkers through further experimentation.

1. Use Causaly's API to ask questions about the effects of genes and proteins on organ systems and cellular functions.

2. Explore the knowledge graph to identify potential safety concerns and toxicological effects.

3. Expedite and improve the design of safety assessments and toxicology experiments.

4. Refine target selection to minimize potential safety risks.

5. Conduct thorough risk assessments based on comprehensive data analysis.

1. Use the Bio Graph API in internal data pipelines to add high-quality data and dimensions of evidence.

2. Pursue questions such as 'What other diseases are the target of this drug indicated in?' and 'How strong is the evidence for that?'

3. Future-proof drug development processes by leveraging the API for data enrichment.

4. Improve the scientific rationale and strength of evidence for drug development decisions.

5. Streamline data pipeline processes for enhanced efficiency.