Validation Methodology: HPA Multi-Agent System

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Overview

This document provides comprehensive validation methodology for the AI multi-agent system described in Part 1: Building an AI Multi-Agent System for Human Protein Atlas.

All validation protocols, reproducibility instructions, and architectural details are documented here for transparency and scientific rigor.

Appendix A: Validation Methodology

A.1 Data Sources and Versions

Human Protein Atlas (HPA)

  • Access method: Public JSON API (https://www.proteinatlas.org/search/{gene}?format=json)
  • Access period: November 2025 - December 2025
  • Data modules utilized:
    • Normal Tissue: Gene expression across 50 tissues (273M+ records)
    • Brain Atlas: Regional specificity data (brain regions, cell types)
    • Blood Atlas: Protein concentrations in blood/plasma
    • Pathology Atlas: Cancer tissue expression patterns
  • Validation approach: Direct API queries to retrieve ground truth for cross-validation

Commercial Antibody Catalog

  • Coverage: 401,855 antibody products
  • Data quality assurance: 85.1% of antibody targets normalized to official HGNC gene symbols via 6-phase pipeline using HGNC alias resolution and MyGene.info API cross-reference validation (integrating HGNC, UniProt, Ensembl, NCBI Gene, and RefSeq)
  • Metadata: Application compatibility (IHC, IF, Western blot, ELISA), reactivity, clonality, validation status

JensenLab DISEASES Database

  • Coverage: 11.5M disease-gene associations across 7,993 diseases
  • Use case: Disease context enrichment for biomarker queries

A.2 Validation Protocol: AHSG Cross-Check Example

Step 1: System Query Execution

Input: "Find liver-specific proteins"
System Processing:
  - Intent classification: tissue_biomarker pattern
  - Multi-metric filtering: tau ≥ 10, fold-enrichment ≥ 1.5, nTPM ≥ 10.0
  - Database query: 20,162 genes searched
  - Results: 19 liver-specific proteins identified
  - Top result: AHSG (ENSG00000145192)

Step 2: HPA Ground Truth Retrieval

API Endpoint: https://www.proteinatlas.org/search/ahsg?format=json
Response Extraction:
  - Tau score: data.summary.tau_score.value = 4319
  - Liver nTPM: data.tissue[tissue="liver"].ntpm.value = 5439.8
  - Reliability: data.tissue[tissue="liver"].reliability = "Enhanced"
  - Blood protein: data.blood_concentration.level = "Medium" (detected)

Step 3: Variance Calculation

Tau Score Variance:
  System: 4,328 | HPA: 4,319
  Variance: |4328 - 4319| / 4319 × 100 = 0.21%

Liver nTPM Variance:
  System: 5,638.7 | HPA: 5,439.8
  Variance: |5638.7 - 5439.8| / 5439.8 × 100 = 3.65%

Reliability Classification:
  System: Enhanced | HPA: Enhanced
  Match: ✓ (exact agreement)

Secretion Status:
  System: Secreted (864 pg/L blood) | HPA: Medium blood concentration
  Match: ✓ (consistent)

Step 4: Acceptance Criteria

  • Variance ≤10% for quantitative metrics (tau, nTPM) = PASS
  • Exact match for categorical classifications (reliability) = PASS
  • Biological coherence (secreted protein detected in blood) = PASS

A.3 Metrics Definitions and Biological Interpretation

Tau Score (τ)

  • Definition: Tissue-specificity metric ranging from 0 (ubiquitous) to theoretical maximum
  • Calculation: Based on expression distribution across all tissues (HPA methodology)
  • Interpretation:
    • τ < 10: Ubiquitously expressed
    • τ 10-100: Low tissue specificity
    • τ 100-1000: Moderate tissue specificity
    • τ > 1000: High tissue specificity
  • Threshold applied: τ ≥ 10 (tissue-enriched), adjusted based on query context
  • Source: HPA summary statistics via JSON API

Fold-Enrichment

  • Formula: fold_enrichment = tissue_ntpm / max(other_tissues_ntpm)
  • Example (AHSG): 
    Liver nTPM: 5,638.7
Highest non-liver tissue (kidney): 1.3 nTPM
Fold-enrichment: 5,638.7 / 1.3 = 4,337×
  • Interpretation:
    • 1.5-4×: Tissue-enriched
    • 4-10×: Tissue-preferential

    • 10×: Highly tissue-specific

  • Threshold applied: ≥1.5× (minimum for tissue enrichment), ≥4× (high confidence)
  • Biological significance: Indicates preferential expression, not absolute exclusivity

nTPM (Normalized Transcripts Per Million)

  • Definition: RNA expression level normalized across samples
  • Range: 0 (not detected) to >10,000 (very high expression)
  • Threshold applied: nTPM ≥ 10.0 (detected expression)
  • Source: HPA RNA consensus dataset (GTEx, HPA, FANTOM5)

HPA Reliability Classification

  • Categories (descending confidence):
    • Enhanced: Antibody validated by orthogonal methods (protein/RNA agreement)
    • Supported: Antibody supported by multiple independent antibodies
    • Approved: Antibody approved by protein array or similar
    • Uncertain: Antibody not validated or uncertain
  • Preference: Enhanced > Supported > Approved (uncertain excluded by default)
  • Source: HPA antibody validation metadata

A.4 Quality Assurance and Evidence-Based Evaluation

Post-Query Validation Process

  1. Automated validation: All biomarker results cross-checked against HPA JSON API immediately after query execution
  2. Evidence rubric applied:
    • ✅ Verified: Claim directly supported by HPA data (variance ≤10%)
    • ⚠️ Partial: Claim partially supported with minor discrepancies
    • 🔍 Requires Review: Claim needs manual expert review
    • ❌ Contradicted: Claim contradicted by HPA ground truth
    • ⚪ Not Applicable: HPA lacks data for this claim
  3. Accuracy calculation: verified_claims / total_verifiable_claims × 100

Test 01 (Liver-Specific Proteins) Evaluation Results

  • Total biological claims: 47
  • Verified (✅): 44 claims
  • Not applicable (⚪): 3 claims (HPA lacks data)
  • Contradicted (❌): 0 claims
  • Accuracy: 44/47 = 93.6%

Hallucination Prevention

  • Requirement: All gene symbols, tau scores, nTPM values, reliability classifications must trace to database records
  • Validation: Cross-reference every quantitative claim against HPA JSON API
  • Outcome: Zero hallucinations detected across 12 benchmark tests (0/139 entities fabricated)

A.5 Reproducibility Instructions

To Independently Verify AHSG Liver Specificity:

  1. Access HPA JSON API: From command line interface (CLI) or use web browser with just the URL only

    curl 'https://www.proteinatlas.org/search/ahsg?format=json'

  2. Extract tau score:

    response[0].tissue_expression.data.summary.tau_score.value
Expected: 4319 (as of December 2025)

  3. Extract liver nTPM:

    response[0].tissue_expression.data.tissue.find(t => t.tissue === "liver").nTPM
Expected: 5439.8 nTPM

  4. Calculate fold-enrichment:

    liver_ntpm = 5439.8
max_other = max(kidney: 1.3, duodenum: 0.5, ...) = 1.3
fold_enrichment = 5439.8 / 1.3 = 4,184×

  5. Verify reliability:

    response[0].tissue_expression.data.tissue.find(t => t.tissue === "liver").reliability
Expected: "Enhanced"

  6. Compare to reported values:

    • System tau: 4,328 vs HPA: 4,319 (0.2% variance) ✓
    • System liver nTPM: 5,638.7 vs HPA: 5,439.8 (3.7% variance) ✓
    • System fold-enrichment: 4,337× vs calculated: 4,184× (3.7% variance) ✓
    • System reliability: Enhanced vs HPA: Enhanced (match) ✓

Note: Minor variance expected due to:

  • Database update timing (the system may use slightly older HPA data)
  • Rounding differences in fold-enrichment calculation
  • Intermediate data transformations in the system

A.6 AI Agent Architecture

System Architecture Diagram:

StageComponentDescription
INPUTNatural Language Query”Find liver-specific proteins”
↓
PHASE 1Planning Agent• Analyzes biological intent
• Recognizes query pattern
• Routes to specialized agent
• 100% routing accuracy
↓
AGENTS8 Specialized AI Agents• Tissue Biomarker
• Brain Biomarker
• Cell Type Marker
• Serum Biomarker
• Blood Biomarker
• Biomarker Validation
• Vendor Specialization
• Generic Exploratory
↓
DATAData Sources• HPA Tissue Expression: 273M+ records, 50 tissues
• Commercial Antibodies: 401,855 products, 7 vendors
• HPA Blood Atlas: Protein concentrations, secretion data
↓
PHASE 2Execution Engine• Multi-Metric Filtering
• Tau scores (≥10 to ≥100)
• Fold-enrichment (≥1.5× to ≥4×)
• nTPM (≥10.0)
• HPA reliability (Enhanced/Supported)
↓
PHASE 3Synthesis Agent• Evidence triangulation
• Conflict resolution
• Biological interpretation
• Source attribution
↓
VALIDATIONHPA Ground Truth Check• Cross-check all claims via HPA JSON API
• Calculate variance from ground truth
• Threshold: ≤10% variance
• If fail → Return to synthesis
↓
OUTPUTValidated Results• Gene list with metrics
• Antibody recommendations
• Biological context
• 93.6% validation accuracy

Figure A.6.1: Three-phase AI agent architecture for biological query processing. The planning agent recognizes query patterns and routes to one of 8 specialized agents (tissue biomarker, brain biomarker, cell type marker, serum biomarker, blood biomarker, biomarker validation, vendor specialization, generic exploratory). Agents query HPA tissue expression data (273M+ records across 50 tissues) and commercial antibody catalogs (401,855 products). Multi-metric filtering applies HPA validation standards (tau scores, fold-enrichment, nTPM, reliability classifications). Synthesis agent performs evidence triangulation and conflict resolution. All results cross-validated against HPA JSON API with ≤10% variance threshold (93.6% validation accuracy across benchmark tests).

Overview: Multi-Agent System for Biological Reasoning

The system uses AI agents—specialized language models trained to reason about biological data—to translate natural language queries into validated database operations. This approach addresses a fundamental challenge: biological queries require understanding context, synonyms, and domain knowledge that traditional keyword matching cannot capture.

Why AI Agents for This Problem:

  1. Biological Language Understanding: Researchers ask questions using varied terminology (“liver-specific,” “hepatic markers,” “liver-enriched genes”). AI agents recognize these as semantically equivalent and map them to appropriate HPA data structures without requiring exact keyword matches.

  2. Context-Aware Thresholding: Different biological questions require different validation thresholds. AI agents assess query context to apply appropriate filters (e.g., tau ≥100 for highly specific markers, tau ≥10 for tissue-enriched genes). This avoids both over-filtering (missing valid results) and under-filtering (including false positives).

  3. Multi-Step Reasoning: Complex queries like “Find ELISA-compatible antibodies for IL-6” require multiple reasoning steps: (1) normalize gene symbol (IL-6 → IL6), (2) identify application requirement (ELISA), (3) search antibody catalog, (4) filter for capture/detection pairs. AI agents decompose and execute these steps without explicit programming for each query type.

Agent Specialization Architecture:

The system employs specialized agents for distinct biological tasks:

  • Planning Agent: Analyzes incoming natural language query, classifies biological intent across 8 patterns (tissue biomarker, brain biomarker, cell type marker, serum biomarker, blood biomarker, biomarker validation, vendor specialization, generic exploratory), and routes to appropriate specialized agents. Achieves 100% routing accuracy (12/12 tests).

  • Tissue Biomarker Agent: Handles tissue-specific protein queries. Applies HPA tissue expression data, calculates fold-enrichment across 50 tissues, filters by tau scores and reliability classifications, cross-references with antibody availability.

  • Brain Biomarker Agent: Specialized for brain region queries (hippocampus, cortex, etc.). Utilizes HPA Brain Atlas data with regional specificity metrics, understands neuroanatomical hierarchies (e.g., cortex contains subregions like motor cortex, visual cortex).

  • Cell Type Marker Agent: Handles cell type-specific queries (T cells, neurons, etc.). Applies cell type enrichment scores, validates against single-cell RNA-seq data when available.

  • Serum/Blood Biomarker Agents: Specialized for biofluid protein queries. Integrates HPA Blood Atlas data, distinguishes serum (cell-free) vs. blood (cellular + cell-free) contexts, prioritizes secreted proteins.

  • Vendor Specialization Agent: Handles antibody procurement queries with vendor constraints. Searches commercial catalog (401,855 products), applies vendor normalization (e.g., “Abcam” includes variations), ranks by quality metrics.

  • Synthesis Agent: Integrates outputs from specialized agents, applies evidence-based reasoning requirements (triangulation, cross-pattern validation, conflict resolution), generates natural language results with biological interpretation and source attribution.

How Agents Coordinate:

  1. Planning Phase: Planning agent receives query, analyzes biological intent, determines execution strategy (sequential vs. parallel), spawns appropriate specialized agents.

  2. Execution Phase: Specialized agents execute in parallel or sequence based on query dependencies. Each agent has access to:

    • Database query tools (HPA tissue expression, antibody catalogs, disease associations)
    • Calculation tools (fold-enrichment, tau scores, quality metrics)
    • Validation tools (HPA JSON API cross-check, biological coherence verification)

  3. Synthesis Phase: Synthesis agent receives all specialized agent outputs, resolves conflicts (e.g., if multiple agents return overlapping results), applies evidence triangulation, generates final response with biological context.

Quality Control Mechanisms:

  • Deterministic Tool Calling: Agents generate database queries that are executed deterministically. The same natural language input produces identical database operations, ensuring reproducibility.

  • Validation Against Ground Truth: All biomarker claims cross-validated against HPA JSON API post-query (see Section A.4). This catches agent reasoning errors before results are returned.

  • Hallucination Prevention: Agents required to cite data sources for all quantitative claims (gene symbols, tau scores, nTPM values). Claims without database evidence are flagged and excluded. Achieved 0% hallucination rate (0/139 entities fabricated) across 12 benchmark tests.

  • Biological Coherence Checks: Agents verify internal consistency (e.g., secreted proteins should be detected in blood, tissue-specific markers should show appropriate fold-enrichment).

Why This Matters for Scientific Accuracy:

Traditional approaches to biological data access use either:

  1. Keyword matching: Brittle, fails on synonyms (“p53” vs “TP53” vs “tumor protein p53”)
  2. Hardcoded queries: Cannot adapt to diverse biological questions without extensive programming

AI agents provide a middle ground:

  • Flexible: Understand biological language and context
  • Validated: Cross-checked against HPA ground truth (93.6% validation accuracy)
  • Transparent: All reasoning traceable to database operations and source data

The agent architecture enables natural language access while maintaining the biological rigor that makes HPA validation data trustworthy. Agents can make mistakes—which is why every result undergoes post-query validation against HPA’s JSON API.

Computational Requirements:

  • Query processing time: 2-6 minutes (average 232.5 seconds)
  • Agent reasoning overhead: ~30-50% of total time (remainder: database queries, validation)
  • Cost per query: ~$0.136 (primarily AI agent reasoning costs)

A.7 Query Processing Workflow (Integration of Agent Architecture)

Query Processing Workflow

  1. Intent Classification:

    • Natural language input analyzed for biological intent
    • Pattern recognition across 8 query types (tissue_biomarker, brain_biomarker, cell_type_marker, serum_biomarker, blood_biomarker, biomarker_validation, vendor_specialization, generic_exploratory)
    • Pattern routing accuracy: 100% (12/12 tests)

  2. Multi-Database Retrieval:

    • HPA tissue expression data: 273M+ records queried
    • Commercial antibody catalog: 401,855 products searched (85.1% normalized to HGNC gene symbols via MyGene.info cross-reference validation using HGNC, UniProt, Ensembl, NCBI Gene, and RefSeq)
    • Disease-gene associations: 11.5M JensenLab DISEASES records available
    • Query execution time: 2-6 minutes depending on complexity

  3. Multi-Metric Filtering:

    • Tau score threshold: ≥10 (tissue-enriched) to ≥100 (highly specific)
    • Fold-enrichment threshold: ≥1.5× (minimum) to ≥4× (high confidence)
    • nTPM threshold: ≥10.0 (detected expression)
    • HPA reliability: Enhanced or Supported preferred
    • Filters applied automatically based on query context

  4. Cross-Database Validation:

    • Results validated against HPA JSON API before returning
    • Antibody availability checked via catalog search
    • Pathway assignments cross-referenced with HPA clusters
    • Biological coherence verified (e.g., secreted proteins detected in blood)

  5. Evidence-Based Synthesis:

    • Results synthesized with biological context
    • Source attribution for all claims (HPA ensembl IDs, tau scores, etc.)
    • Confidence indicators provided (reliability classifications, fold-enrichment ratios)
    • Procurement guidance included (antibody recommendations, vendor options)

Statistical Summary (12-Test Benchmark)

  • Total queries executed: 12
  • Success rate: 100% (12/12)
  • Pattern routing accuracy: 100% (12/12 correct classifications)
  • Total biological entities discovered: 139 (proteins, genes, antibodies)
  • Average query duration: 232.5 seconds (range: 133s - 371s)
  • Average validation accuracy: 93.6% (Test 01, only test with detailed claim-level validation)
  • Hallucination rate: 0% (0/139 entities fabricated)