Is your target a true disease driver or just a correlated signal?

In biology, the challenge is not generating data or predictions; it’s making confident intervention decisions when mechanisms are incomplete, and evidence is fragmented. While correlation-based and generative AI can forecast outcomes, decisions without causal reasoning remain fragile across discovery, translation, and clinical development.

bAIcis®, BPGbio’s causal biology engine, is designed for these moments.

Using Bayesian network inference, it learns causal structure directly from complex biomedical data with or without prior knowledge. Built on a Bayesian foundation, bAIcis® moves beyond vague, black-box predictions on the effect of a potential intervention to providing explainable, mechanistic insights describing how interventions propagate their effects throughout the entire system.

As the causal reasoning engine within the NAi Interrogative Biology® platform, bAIcis® transforms heterogeneous multi-omic and clinical data into interpretable cause-and-effect models that support high-confidence therapeutic decisions.

Predictive AI excels at pattern recognition, but drug development requires understanding which biological changes will drive positive clinical outcomes.

bAIcis® identifies directional relationships between biological variables and outcomes, enabling teams to evaluate interventions under uncertainty with explicit, testable assumptions. This allows researchers to:

• distinguish true drivers from downstream effects
• evaluate hypotheses before experimentation
• quantify uncertainty transparently
• update conclusions as new evidence emerges

In short, bAIcis® answers what predictive AI cannot: What happens if we intervene?

Unlike correlation-based approaches, bAIcis® implements Bayesian causal structure learning to build interpretable biological networks grounded in causal relationships, not just associations.

bAIcis®:

• learns causal structure from heterogeneous data under defined constraints and with or without prior knowledge
• enables counterfactual (“what-if”) reasoning
• quantifies uncertainty using Bayesian probability
• maintains full traceability through a fully interpretable, explainable algorithm

The result is a transparent, decision-grade model suited for translational and regulatory-aligned evidence generation.

bAIcis® integrates multi-omic, phenotypic, clinical, and real-world data into continuously evolving causal models with full traceability. Its workflow includes:

• Evidence integration: unify diverse biomedical datasets – molecular, phenotypic, and clinical, and real-world datasets
• Causal structure learning: infer directional relationships with or without prior knowledge
• Intervention simulation: support reasoning of counterfactual scenarios
• Versioned models: update models as new data emerges while maintaining traceability

The result is a decision-grade causal framework where assumptions can be inspected, conclusions challenged and improved over time.

bAIcis® Within The NAi Platform

bAIcis® powers decision-grade reasoning within the NAi Interrogative Biology® platform. Combined with NAi’s clinically annotated biobank, multi-omics integration, spatial biology, and high-performance computing, it enables identification of disease drivers directly from patient biology and translation into actionable therapeutic strategies with high confidence.

From Prediction to Intervention

When therapeutic decisions require interpretable structure—not just statistical prediction—bAIcis® provides the causal reasoning needed to move forward with confidence.

 

Try bAIcis® today to see how it can impact your project and its success!