Cancer doesn’t follow patterns.
It follows causes.
Most AI in healthcare finds statistical correlations — which drugs are associated with which outcomes. Galen is built differently. It traces the causal mechanisms underneath: why a mutation drives resistance, why a treatment works, and why it might stop working.
That’s not a feature. It’s the foundation everything else is built on.
Why cancer demands causal reasoning.
Cancer isn’t one disease — it’s hundreds, each driven by a unique constellation of mutations, pathway disruptions, and microenvironmental factors. Three properties make it uniquely unsuited to pattern-matching approaches.
Cancer evolves. Tumors develop resistance to therapy through specific molecular mechanisms — a point mutation in the drug-binding pocket, amplification of a bypass pathway, epigenetic reprogramming. A system that only knows “drug X stopped working” can’t tell you why, or what to try next.
Cancer is heterogeneous. Two patients with the same diagnosis can have entirely different molecular profiles, and the same mutation can have different consequences depending on what else is present. Statistical averages obscure the biology that matters for an individual case.
Cancer exploits dormancy and metastasis through mechanisms that are causal, not correlative — they involve specific signaling cascades, immune evasion strategies, and metabolic adaptations that must be understood mechanistically to be addressed.
These aren’t edge cases. They’re the norm. And they’re why Galen is built on causal models, not statistical pattern-matching.
The difference between correlation and explanation.
Consider a patient whose tumor carries an EGFR T790M mutation. A statistical model flags that this mutation correlates with poor response to first-generation inhibitors. That’s true, but it doesn’t tell you why — or what to do about it.
Galen traces the mechanism: the T790M substitution alters the ATP-binding pocket, reducing erlotinib affinity. Osimertinib was designed to bind despite that structural change. That causal chain — from mutation to structural consequence to therapeutic implication — holds up where pattern matching breaks down: in rare mutations, unusual combinations, and newly approved therapies where historical data is thin.
Example — Galen’s causal trace
Each step is grounded in experimental evidence — not generated, not inferred from text patterns.
Three levels of understanding.
Galen reasons using formal causal inference — following Judea Pearl’s Causal Hierarchy, the same mathematical framework used in modern causal science.
Observation — What happens?
Patients with EGFR L858R mutations respond to osimertinib 78% of the time. This is what most AI stops at — statistical association.
Intervention — What happens if we act?
If we administer osimertinib, the mutant EGFR kinase is inhibited, downstream MAPK signaling is suppressed, and tumor proliferation slows. Galen traces the mechanism of action, not just the outcome.
Counterfactual — What would have happened?
If the patient had carried a C797S co-mutation instead, osimertinib resistance would emerge via a different binding mechanism — requiring a different therapeutic strategy. This is where causal reasoning becomes essential for personalized treatment.
A living map of cancer biology.
At the core of Galen is a knowledge graph — a structured map where genes, mutations, drugs, pathways, and cancer types are connected by evidence-grounded relationships. It’s not a database you search. It’s a web of verified causation that Galen reasons across.
Every connection carries its provenance: the source, the evidence tier, and the causal reasoning that established it. A mutation matters because of the pathways it disrupts. A drug matters because of the targets it binds. The relationships between entities are where clinical insight lives.
Today, Galen’s knowledge graph contains 855,000 entities and 7,700,000 relationships across 10+ databases — and it grows every hour.
How Galen thinks
Galen traces the science from your DNA change, through the biology, to the right treatments — no guessing.
Not all evidence is equal.
Every piece of knowledge in Galen’s graph is tagged with the strength of the evidence behind it. You always know whether an answer rests on clinical trial data, published research, or computational inference.
Clinical evidence
Findings from clinical trials, FDA-approved treatments, and established medical guidelines. The strongest form of evidence.
Research evidence
Published laboratory studies, peer-reviewed research, and emerging findings that show strong promise but haven’t yet reached clinical validation.
Computational evidence
Connections identified through analysis of biological pathways, molecular relationships, and data patterns. Valuable for understanding context, clearly labeled as computational.
An intelligence that never stops researching.
Galen doesn’t wait for a human to point it at a paper. An autonomous research agent runs 24 hours a day — reading newly published studies, integrating clinical trial results, and incorporating drug approvals as they happen. The knowledge graph grows every single hour.
When a new resistance mechanism is published, when a combination therapy shows unexpected promise, when a biomarker study redefines who benefits from a treatment — Galen absorbs it and traces how it connects to everything it already knows. You get answers grounded in today’s evidence, not a snapshot from months ago.
Intelligence that compounds.
Galen doesn’t just accumulate data. It evaluates its own reasoning, identifies where its understanding is weakest, and redirects its research toward the frontiers of what it doesn’t yet know.
Testable predictions.
Galen generates falsifiable hypotheses about drug sensitivity, gene essentiality, and treatment mechanisms — then validates them against experimental data. It holds itself accountable to evidence.
Compositional depth.
Galen connects findings across domains that are typically siloed — linking genomic mutations to pathway disruptions to drug mechanisms to clinical trial outcomes. The intelligence isn’t in any one database. It’s in the causal connections between all of them.
Recursive self-improvement.
Galen reflects on what it’s learned, identifies gaps in its own reasoning, and proposes improvements to its own research strategy. Every cycle makes the next cycle more effective.
Built on the world’s biomedical knowledge.
Peer-reviewed databases. Continuously integrated. Every source traced.