The document discusses the use of retrieval-augmented generation (RAG) in developing biomedical knowledge graphs and chatbots utilizing large language models (LLMs). It highlights challenges faced by LLMs, such as knowledge cutoffs and hallucinations, and proposes solutions like predefined Cypher templates for improved accuracy. Several examples of queries related to diseases and genetic variants are provided, along with resources for further exploration.

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GraphRAG for Life Science
By Tomaz Bratanic

2. 2
LLM-based chatbots
Using the retrieval-augmented generation

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LLMs are great, but:
• Knowledge
cutoff date
• Hallucinations
• Lack of
information
source
• Lack of
domain-specific
or private
information

4. Retrieval-augmented generation (in-context passing) flow

5. Chat with your PDF - Vector similarity search

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● Consists of nodes
and relationships
● Nodes are used to
represent entities,
concepts, and more
● Relationships
describe the context
and how it is
connected to other
nodes
Biomedical knowledge graph

8. Constructing a biomedical knowledge graph
Using existing biomedical ontologies combined with NLP extraction

9. Graph RAG
MATCH(g:Gene)
WHERE g.id = $gene
MATCH (g)-[r:VARIANT_FOUND_IN_GENE]-(v:Known_variant)
WHERE NOT v.clinical_relevance = '-' AND NOT v.disease = '-'
RETURN g.id as gene, v.disease AS disease,
v.pvariant_id AS variant_id, v.clinical_relevance as relevance
LIMIT 20

10. Generating Cypher with LLM lack accuracy and robustness

11. Solution: Agentic tools with predefined Cypher templates

12. Providing agent with tools

13. Agent tool definition
MATCH (p:Protein)-[r:ASSOCIATED_WITH]-(d:Disease)
WHERE d.name CONTAINS $disease
OPTIONAL MATCH
(p)-[r2:ASSOCIATED_WITH]->(t:Tissue)
RETURN d.name as disease, p.name as drug_target,
t.name as expressed_tissue
LIMIT 10
class DiseaseTissueInput(BaseModel):
disease: str = Field(description="disease mentioned in
the question")
class DiseaseTissueTool(BaseTool):
name = "DiseaseTissue"
description = "useful for when you need to find tissues
where proteins with a specific disease are expressed"
Parameterized Cypher
statement
Parameter description
Tool description

14. 14
Demo time
● What are associated foods with cancer?
● What are known clinically relevant gene variants for gene CLN3?
● In which tissues are expressed proteins associated with prostate carcinoma?

15. 15
Questions
Repository: https://github.com/tomasonjo/clinical-agent
Slides:
https://www.slideshare.net/slideshow/graphrag-for-life-science-to-increase-llm-acc
uracy/269616483
Email: tomaz.bratanic@neo4j.com
LinkedIn: https://www.linkedin.com/in/tomaz-bratanic-a58891127/