/ graph models
Capture & Operationalize Scientific Reasoning
With Graph Models, complex decisions such as target prioritization can be broken down into explicit and testable evidence paths.
Evidence Ranking & Prioritization
Uncertainy-Aware Reasoning
Parallel & Modular
/ Feature
Evidence Ranking & Prioritization
Build custom prioritization models that reflect your scientific judgement
Individually tunable parameters for precision
Aggregation formulas that work out of the box
Capture priorities such as expression levels, pathway involvement, or clinical observations in a way that matches your team’s perspective.
/ Feature
Uncertainty Aware Reasoning
Quantify support and conflict across thousands of experiments. Graph Models calculate probabilities where data disagrees, making degrees of uncertainty visible and measurable instead of hidden.
Individually configure confidence levels based on context
Draw upon collections of factual data points to make better conclusions
Express conditionals and embed probabilistic priors
/ Feature
Parallel & Modular
Graph models can be built, developed, and deployed simultaneously.
Create and maintain many models simultaneously
Represent different strategies, perspectives, and dogmas
Easily compare, refine, and update models as new data emerges
/ metrics
Systems that deliver real impact.
With BioBox, biopharma teams don’t just manage data, they multiply their scientific output.
Time recovered for translational scientists:
32%
Scientists and decision makers save 5-10 hours every week across tasks in preclinical R&D.
Hypothesis testing throughput increased by:
Up to 100x
Using graph models built to reason like human experts, R&D teams are able to prosecute massive amounts of hypotheses per unit time.
< 3 weeks
to full team onboarding
100%
customer ownership of data
24/7
support and uptime monitoring
99.8% uptime
platform reliability and performance