An ontology, in the context of biomedical informatics, is a formal representation of knowledge within a domain. It defines the types of entities that exist (drugs, proteins, pathways, diseases), the relationships between them (inhibits, participates in, causes), and the rules governing those relationships.

A causal ontology goes further. It does not just describe associations — it encodes directionality and mechanism. "Lisinopril inhibits ACE" is a causal statement. "ACE participates in bradykinin degradation" is a mechanistic relationship. Chaining these statements together creates a causal explanation: lisinopril causes cough because it inhibits ACE, which normally degrades bradykinin, and accumulated bradykinin sensitizes airway nerves.

Why Graphs Are the Right Data Structure

Biological relationships are inherently graph-structured. A single drug can bind multiple targets. A single protein can participate in dozens of pathways. A single pathway can be implicated in hundreds of diseases. Trying to represent this in tabular form leads to explosive redundancy or information loss.

In a graph, each entity is a node and each relationship is an edge. The graph can be traversed, searched, and reasoned over. You can ask questions like: "What is the shortest path from Drug X to Side Effect Y?" or "Which pathways are shared between Drug A and Drug B?"

The BioMed Reasoning API builds these graphs on demand from seven curated databases. When you query a drug, the system constructs a subgraph of relevant entities and relationships, then traverses it to find causal chains.

Where the Data Comes From

The ontology is not invented — it is assembled from existing biomedical knowledge bases:

• PubChem provides drug identity, structure, and synonyms for over 111 million compounds.
• ChEMBL provides curated bioactivity data for drug-target interactions from published literature.
• UniProt provides protein sequence, function, and gene annotations for the human proteome.
• Reactome provides curated biological pathways for Homo sapiens with over 2,600 pathways.
• CTD provides over 1.6 million curated chemical-gene interactions and chemical-disease associations.
• OpenFDA FAERS provides real-world adverse event reports from the FDA's post-market surveillance system.
• Open Targets provides target-disease association scores integrating genetic and literature evidence.

The Technical Moat

Building a biomedical causal ontology is not a weekend project. It requires understanding the data models of each upstream database, handling inconsistencies across sources, normalizing entity names (a drug can have dozens of synonyms), and designing graph traversal algorithms that weight evidence quality.

This complexity is also what makes it defensible. An LLM can be fine-tuned on biomedical text in a few days. But building a system that constructs verifiable causal chains from structured data, scores confidence based on evidence density, and attributes every claim to a specific source — that requires deep domain expertise and careful engineering.

The BioMed Reasoning API encodes this expertise into infrastructure. Instead of every health AI team building their own biomedical knowledge graph from scratch, they can query a single API that returns structured, source-backed reasoning.

Looking Forward

As more biomedical data becomes available — from genomics, proteomics, electronic health records, and clinical trials — the ontology will grow. New relationship types, new entity categories, and new evidence sources will be integrated. The graph becomes richer over time, and so do the causal chains it can produce.

This is the long-term vision: a continuously expanding biomedical reasoning layer that any health AI system can plug into. Not a chatbot. Not a search engine. A reasoning infrastructure.