biomarker-multi-agent-discovery

Research

Use when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and clinical evidence synthesis to produce ranked biomarker panels.

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Biomarker Multi-Agent Discovery

When to use this skill

Complex biomarker discovery requiring multiple analysis modalities
Coordinating database queries with statistical survival analysis
Synthesizing findings from literature, pathways, and clinical data into a biomarker panel
Questions that span multiple sub-domains (e.g., "find best biomarker for survival in chemo patients and show evidence")

Architecture: Agents-as-Tools Pattern

The orchestrator dispatches to specialized sub-agents, each wrapped as a tool:

Orchestrator (Supervisor)
  |-- biomarker_database_analyst_agent  -> SQL queries on clinical genomic data
  |-- clinical_evidence_research_agent  -> PubMed + Knowledge Base search
  |-- statistician_agent                -> Survival regression, Kaplan-Meier plots
  |-- medical_imaging_agent             -> Radiomics biomarker extraction

Cross-agent data sharing uses AgentCore Memory: the database agent stores query results, and downstream agents (statistician) retrieve them automatically.

Orchestration Workflow

Step 1: Classify the user query

Map the question to required sub-agents:

Query type	Agents needed	Sequence
Demographics / counts	Database analyst only	Single call
Literature evidence	Clinical evidence researcher only	Single call
Statistical analysis (p-values, survival)	Database analyst -> Statistician	Sequential
Imaging biomarkers	Database analyst -> Medical imaging	Sequential
Comprehensive discovery	All agents	Multi-step
Pathway interpretation	Database analyst -> Literature	Sequential

Step 2: Execute database queries first

For any analysis requiring patient data:

Call biomarker_database_analyst_agent with the data retrieval question
Results are automatically stored in shared memory
Include required columns: survival_status, survival_duration, biomarker expression values

Example dispatch:

Query: "What are the top 5 biomarkers with overall survival for chemo patients?"
-> Database agent: "Query all records including survival status, survival duration in years, and gene expression values for patients where chemotherapy = 'Yes'"

Step 3: Feed results to downstream agents

For statistical analysis:

-> Statistician agent: "Fit a survival regression model on the query results"

The statistician retrieves data from memory automatically. No S3 path needed.

For visualization:

-> Statistician agent: "Generate a bar chart of the top 5 biomarkers by p-value"
-> Statistician agent: "Plot Kaplan-Meier curve for GDF15 with threshold 10"

For literature validation:

-> Clinical evidence researcher: "Search PubMed for evidence on GDF15 as a biomarker in NSCLC"

Step 4: Synthesize findings into ranked biomarker panel

Combine outputs from all agents into a consolidated report:

Biomarker Panel Report
=====================
1. [Gene] - p-value: X, HR: Y
   - Pathway: [from pathway analysis]
   - Literature: [N publications supporting]
   - Clinical significance: [interpretation]

2. [Gene] - p-value: X, HR: Y
   ...

Ranking criteria (in priority order):

Statistical significance (lowest p-value from Cox regression)
Clinical significance (hazard ratio magnitude)
Pathway relevance (membership in disease-associated pathways)
Literature support (number of supporting publications)
Biological plausibility (protein function matches disease mechanism)

Step 5: Generate actionable recommendations

For each top biomarker, provide:

Measurement method (IHC, RNA-seq, blood test)
Patient stratification threshold (expression cutoff)
Potential clinical utility (prognostic vs. predictive vs. diagnostic)
Next validation steps (cohort size, assay development)

Tool Dispatch Reference

Agent	Tools	Input	Output
Database Analyst	get_schema, query_redshift, refine_sql	Data question	Query results (auto-stored in memory)
Clinical Evidence	query_pubmed, retrieve	Evidence question	Literature summary with citations
Statistician	run_code, plot_kaplan_meier, fit_survival_regression	Analysis request	Regression, charts (S3 paths), p-values
Medical Imaging	compute_imaging_biomarker, analyze_imaging_biomarker	Patient IDs	Radiomics features (sphericity, elongation)

Example Multi-Step Sequences

"Find best biomarker for survival in chemo patients with visualization":

Database agent -> Statistician (Cox regression) -> Statistician (Kaplan-Meier) -> Literature -> Synthesize

"Compare imaging biomarkers for patients with lowest GDF15":

Database agent (find patients) -> Medical imaging (compute + visualize) -> Synthesize

Conventions

Always explain the multi-step plan to the user before executing
Present results from each agent separately, then provide consolidated summary
Include S3 paths for any generated charts or images
When agents fail, explain which step failed and what alternatives exist
Medical/statistical concepts must be explained in accessible language
Memory events expire after 3 days -- no manual cleanup needed