Biomedical Research: Tracking Disease Paths

Human biology is the ultimate Knowledge Graph. Every Gene interacts with a Protein, which causes a Biological Pathway, which manifests as a Symptom, which is treated by a Drug. When a researcher reads a paper about a "New Side Effect," that data is useless unless it is linked to the rest of the protein network. Graph RAG turns a library of 30 million medical papers (PubMed) into a Reasoning Map for Discovery.

In this lesson, we will look at Bio-Graphs. We will learn how to extract [:TREATS], [:CAUSES], and [:ASSOCIATED_WITH] relationships from scientific abstracts. We will see how an AI can identify "Drug Repurposing" opportunities by finding a path from a known drug to a new disease via a shared protein node.

---

1. The Bio-Medical Graph Schema

• (:Gene) {sequence, expression_level}
• (:Disease) {symptoms, prevalence}
• (:Drug) {molecule_type, manufacturer}
• (:Protein) {function, amino_acid_chain}
• (:Publication) {journal, impact_factor}

---

2. Drug Repurposing (The Path-Discovery Goal)

Traditional research takes 10 years to find a new drug. Graph RAG can find an existing drug that might work for a new disease in seconds.

• The Logic:
  1. Drug A treats Disease X.
  2. Disease X involves Protein Y.
  3. Disease Z (new) also involves Protein Y.
  4. Hypothesis: Drug A might treat Disease Z.

---

3. Handling "Scientific Confidence"

In science, not every claim is a "Fact." Some are "Hypotheses." In our graph, we use Relationship Weights (Module 11) based on:

• P-Value: The statistical strength of the claim.
• Impact Factor: The reputation of the journal.
• Citations: How many other scientists agree?

AI Synthesis: "While Drug A is linked to Protein Y, the evidence is based on one study with a small sample size. I suggest cross-verifying with the ClinicalTrials graph."

graph LR
    D[Drug: Alpha] -->|Treats| DX[Disease: X]
    DX ---|Involves| P[Protein: Y]
    DZ[Disease: Z] ---|Involves| P
    D -.->|HYPOTHESIS| DZ
    
    style DZ fill:#f4b400,color:#fff
    style D fill:#34A853,color:#fff
    note[The AI proposes a 'Logical Leap' based on the shared Protein bridge]

---

4. Implementation: Finding Potential Cross-Domain Links

MATCH (d:Disease {name: 'Diabetes'})-[:INVOLVES]->(p:Protein)
MATCH (other:Disease)-[:INVOLVES]->(p)
WHERE other.name <> 'Diabetes'
MATCH (treatment:Drug)-[:TREATS]->(other)
RETURN treatment.name, other.name, p.name;

// This query finds drugs used for OTHER diseases 
// that share proteins with Diabetes.

---

5. Summary and Exercises

Biomedical Graph RAG provides the "Global Map of Life."

• Cross-Domain Discovery links distant concepts (Gene -> Drug).
• Confidence Weighting manages the "Noise" of scientific hypotheses.
• Rapid Hypothesis Generation accelerates the drug discovery pipeline.
• Traceability: A researcher can click on any AI-claimed link to see the exact PubMed paper that supports it.

Exercises

1. Discovery Task: You are researching "Alzheimer's." What are 3 "Node Types" you would want to connect to it in your graph? (e.g., Amyloid Plaques, Specific Genes, Behavioral Symptoms).
2. The "Confidence" Score: If one paper says "Drug X causes Symptom Y" and another says "No it doesn't," how would you represent this in the graph? (Hint: See 'Conflict Resolution' in Module 12).
3. Visualization: Draw a 3-step chain connecting a "Gene" to a "Drug."

In the next lesson, we will look at financial vertical data: Financial Audit: The Paper Trail Graph.