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Building a BioCypher-Powered AI Agent for Biomedical Knowledge Graph Generation and Querying
This tutorial implements the BioCypher AI Agent, a powerful tool designed for building, querying, and analyzing biomedical knowledge graphs using the BioCypher framework. By combining the strengths of BioCypher, a high-performance, schema-based interface for biological data integration, with the flexibility of NetworkX, this tutorial empowers users to simulate complex biological relationships such as gene-disease associations, drug-target interactions, and pathway involvements. The agent also includes capabilities for generating synthetic biomedical data, visualizing knowledge graphs, and performing intelligent queries, such as centrality analysis and neighbor detection.
Target Audience Analysis
The target audience for this tutorial includes:
- Biomedical researchers seeking advanced data analysis tools.
- Data scientists interested in applying AI to biomedical contexts.
- Business managers in healthcare looking for insights into drug development and disease associations.
Pain Points: Users may struggle with integrating diverse biological datasets and require efficient querying methods to extract meaningful insights.
Goals: The audience aims to enhance their understanding of biological relationships through data visualization and intelligent querying.
Interests: Users are likely interested in the latest advancements in AI applications within biomedical research and effective data management techniques.
Communication Preferences: The audience prefers clear, structured content with practical examples and code snippets that can be directly applied to their work.
Implementation of the BioCypher AI Agent
We begin by installing the essential Python libraries required for our biomedical graph analysis:
!pip install biocypher pandas numpy networkx matplotlib seabornNext, we import the necessary modules to set up our development environment:
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import json
import random
from typing import Dict, List, Tuple, AnyWe attempt to import the BioCypher framework, which provides a schema-based interface for managing biomedical knowledge graphs. If the import is successful, we enable BioCypher features; otherwise, we gracefully fall back to a NetworkX-only mode:
try:
from biocypher import BioCypher
from biocypher._config import config
BIOCYPHER_AVAILABLE = True
except ImportError:
print("BioCypher not available, using NetworkX-only implementation")
BIOCYPHER_AVAILABLE = FalseDefining the BiomedicalAIAgent Class
We define the BiomedicalAIAgent class as the core engine for analyzing biomedical knowledge graphs using BioCypher:
class BiomedicalAIAgent:
"""Advanced AI Agent for biomedical knowledge graph analysis using BioCypher"""
def __init__(self):
if BIOCYPHER_AVAILABLE:
try:
self.bc = BioCypher()
self.use_biocypher = True
except Exception as e:
print(f"BioCypher initialization failed: {e}")
self.use_biocypher = False
else:
self.use_biocypher = False
self.graph = nx.Graph()
self.entities = {}
self.relationships = []
self.knowledge_base = self._initialize_knowledge_base()We initialize a sample biomedical knowledge base:
def _initialize_knowledge_base(self) -> Dict[str, List[str]]:
return {
"genes": ["BRCA1", "TP53", "EGFR", "KRAS", "MYC", "PIK3CA", "PTEN"],
"diseases": ["breast_cancer", "lung_cancer", "diabetes", "alzheimer", "heart_disease"],
"drugs": ["aspirin", "metformin", "doxorubicin", "paclitaxel", "imatinib"],
"pathways": ["apoptosis", "cell_cycle", "DNA_repair", "metabolism", "inflammation"],
"proteins": ["p53", "EGFR", "insulin", "hemoglobin", "collagen"]
}Next, we generate synthetic biomedical data:
def generate_synthetic_data(self, n_entities: int = 50) -> None:
print(" Generating synthetic biomedical data...")
for entity_type, items in self.knowledge_base.items():
for item in items:
entity_id = f"{entity_type}_{item}"
self.entities[entity_id] = {
"id": entity_id,
"type": entity_type,
"name": item,
"properties": self._generate_properties(entity_type)
}
entity_ids = list(self.entities.keys())
for _ in range(n_entities):
source = random.choice(entity_ids)
target = random.choice(entity_ids)
if source != target:
rel_type = self._determine_relationship_type(
self.entities[source]["type"],
self.entities[target]["type"]
)
self.relationships.append({
"source": source,
"target": target,
"type": rel_type,
"confidence": random.uniform(0.5, 1.0)
})Building the Knowledge Graph
We build the knowledge graph using BioCypher or NetworkX:
def build_knowledge_graph(self) -> None:
print(" Building knowledge graph...")
if self.use_biocypher:
try:
for entity_id, entity_data in self.entities.items():
self.bc.add_node(
node_id=entity_id,
node_label=entity_data["type"],
node_properties=entity_data["properties"]
)
for rel in self.relationships:
self.bc.add_edge(
source_id=rel["source"],
target_id=rel["target"],
edge_label=rel["type"],
edge_properties={"confidence": rel["confidence"]}
)
print(" BioCypher graph built successfully")
except Exception as e:
print(f"BioCypher build failed, using NetworkX only: {e}")
self.use_biocypher = False
for entity_id, entity_data in self.entities.items():
self.graph.add_node(entity_id, **entity_data)
for rel in self.relationships:
self.graph.add_edge(rel["source"], rel["target"],
type=rel["type"], confidence=rel["confidence"])
print(f" NetworkX graph built with {len(self.graph.nodes())} nodes and {len(self.graph.edges())} edges")Performing Intelligent Queries
We include functions for analyzing drug targets, disease-gene associations, pathway connectivity, and network centrality:
def intelligent_query(self, query_type: str, entity: str = None) -> Dict[str, Any]:
print(f" Processing intelligent query: {query_type}")
if query_type == "drug_targets":
return self._find_drug_targets()
elif query_type == "disease_genes":
return self._find_disease_associated_genes()
elif query_type == "pathway_analysis":
return self._analyze_pathways()
elif query_type == "centrality_analysis":
return self._analyze_network_centrality()
elif query_type == "entity_neighbors" and entity:
return self._find_entity_neighbors(entity)
else:
return {"error": "Unknown query type"}Visualizing the Knowledge Graph
We visualize the knowledge graph:
def visualize_network(self, max_nodes: int = 30) -> None:
print(" Creating network visualization...")
nodes_to_show = list(self.graph.nodes())[:max_nodes]
subgraph = self.graph.subgraph(nodes_to_show)
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(subgraph, k=2, iterations=50)
node_colors = []
color_map = {"genes": "red", "diseases": "blue", "drugs": "green",
"pathways": "orange", "proteins": "purple"}
for node in subgraph.nodes():
entity_type = self.entities[node]["type"]
node_colors.append(color_map.get(entity_type, "gray"))
nx.draw(subgraph, pos, node_color=node_colors, node_size=300,
with_labels=False, alpha=0.7, edge_color="gray", width=0.5)
plt.title("Biomedical Knowledge Graph Network")
plt.axis('off')
plt.tight_layout()
plt.show()Running the Analysis Pipeline
We wrap up the AI agent workflow with a streamlined analysis pipeline:
def run_analysis_pipeline(self) -> None:
print(" Starting BioCypher AI Agent Analysis Pipeline\n")
self.generate_synthetic_data()
self.build_knowledge_graph()
print(f" Graph Statistics:")
print(f" Entities: {len(self.entities)}")
print(f" Relationships: {len(self.relationships)}")
print(f" Graph Nodes: {len(self.graph.nodes())}")
print(f" Graph Edges: {len(self.graph.edges())}\n")
analyses = [
("drug_targets", "Drug Target Analysis"),
("disease_genes", "Disease-Gene Associations"),
("pathway_analysis", "Pathway Connectivity Analysis"),
("centrality_analysis", "Network Centrality Analysis")
]
for query_type, title in analyses:
print(f" {title}:")
results = self.intelligent_query(query_type)
self._display_results(results)
print()
self.visualize_network()
print(" Analysis complete! AI Agent successfully analyzed biomedical data.")Exporting the Knowledge Graph
We ensure that the resulting graph can be saved in both JSON and GraphML formats for further use:
def export_to_formats(self) -> None:
if self.use_biocypher:
try:
print(" Exporting BioCypher graph...")
print(" BioCypher export completed")
except Exception as e:
print(f"BioCypher export failed: {e}")
print(" Exporting NetworkX graph to formats...")
graph_data = {
"nodes": [{"id": n, **self.graph.nodes[n]} for n in self.graph.nodes()],
"edges": [{"source": u, "target": v, **self.graph.edges[u, v]}
for u, v in self.graph.edges()]
}
with open("biomedical_graph.json", "w") as f:
json.dump(graph_data, f, indent=2, default=str)
nx.write_graphml(self.graph, "biomedical_graph.graphml")
print(" Graph exported to JSON and GraphML formats")Conclusion
Through this advanced tutorial, we gain practical experience working with BioCypher to create scalable biomedical knowledge graphs and perform insightful biological analyses. The dual-mode support ensures that even if BioCypher is unavailable, the system gracefully falls back to NetworkX for full functionality. The ability to generate synthetic datasets, execute intelligent graph queries, visualize relationships, and export in multiple formats showcases the flexibility and analytical power of the BioCypher-based agent. Overall, this tutorial exemplifies how BioCypher can serve as a critical infrastructure layer for biomedical AI systems, making complex biological data both usable and insightful for downstream applications.
Check out the Codes here. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
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