---name: mrd-edge-detection-agent description: Ultra-sensitive AI-powered molecular residual disease detection using MRD-EDGE deep learning for sub-0.001% VAF ctDNA detection and early relapse prediction. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• mrd-edge-detection-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

MRD-EDGE Detection Agent

The MRD-EDGE Detection Agent implements the MRD-EDGE (Enhanced Detection of ctDNA through Genomic Error suppression) deep learning algorithm for ultra-sensitive molecular residual disease detection. This AI-powered approach achieves unprecedented sensitivity in predicting cancer recurrence, detecting ctDNA at levels below 0.001% VAF with zero false negatives in validation studies.

When to Use This Skill

• When standard ctDNA assays show negative but MRD is suspected.
• For ultra-sensitive post-surgical MRD monitoring.
• To detect relapse at the earliest possible timepoint.
• When monitoring therapy response in minimal disease settings.
• For research studies requiring highest sensitivity MRD detection.

Core Capabilities

1. Ultra-Sensitive Detection: Detect ctDNA at 0.0001-0.001% VAF levels.

2. Deep Learning Error Suppression: AI-powered sequencing error filtering.

3. Integrated Noise Modeling: Patient-specific background noise estimation.

4. Multi-Feature Integration: Combine mutations, fragmentation, methylation.

5. Zero False Negative Design: Optimized for sensitivity while controlling specificity.

6. Longitudinal Tracking: Monitor MRD over time with confidence intervals.

MRD-EDGE Algorithm Components

Sensitivity Comparison

Workflow

1. Input: Deep sequenced cfDNA (>30,000x), tumor WES, matched normal.

2. Preprocessing: UMI deduplication, duplex consensus, quality filtering.

3. Noise Modeling: Patient-specific error profile estimation.

4. Feature Extraction: Mutations, fragments, methylation signals.

5. Deep Learning Inference: MRD-EDGE neural network prediction.

6. Bayesian Integration: Combine features with uncertainty.

7. Output: MRD probability, detected variants, confidence intervals.

Example Usage

User: "Run MRD-EDGE analysis on this post-surgical colorectal cancer patient's plasma sample."

Agent Action:

python3 Skills/Oncology/MRD_EDGE_Detection_Agent/mrd_edge_detect.py \
    --cfdna_bam plasma_cfDNA.bam \
    --tumor_vcf primary_tumor_mutations.vcf \
    --normal_bam matched_normal.bam \
    --coverage_depth 50000 \
    --cancer_type colorectal \
    --model_weights mrd_edge_v2.pt \
    --output mrd_edge_results/

Input Requirements

Output Components

Deep Learning Architecture

Feature Categories

Clinical Validation

AI/ML Components

Error Suppression Network:

• Convolutional layers for sequence context
• Recurrent layers for read-level features
• Attention for cross-read patterns

Bayesian Integration:

• Prior from tumor mutational burden
• Likelihood from detected signals
• Posterior probability of MRD

Training Strategy:

• Semi-supervised with spike-in controls
• Hard negative mining from CHIP
• Transfer learning across cancer types

Prerequisites

• Python 3.10+
• PyTorch 2.0+
• UMI-tools, fgbio for UMI processing
• bcftools, samtools
• MRD-EDGE model weights
• High-memory compute (>64GB RAM)
• GPU recommended

Related Skills

• ctDNA_Dynamics_MRD_Agent - Longitudinal MRD tracking
• Liquid_Biopsy_Analytics_Agent - Comprehensive liquid biopsy
• CHIP_Clonal_Hematopoiesis_Agent - CHIP filtering
• Tumor_Heterogeneity_Agent - Clonal tracking

Quality Control Metrics

Special Considerations

1. Sample Quality: Requires high-quality cfDNA extraction
2. Sequencing Depth: Deep sequencing essential for sensitivity
3. CHIP Exclusion: Must filter clonal hematopoiesis variants
4. Tumor Heterogeneity: Track clonal and subclonal mutations
5. Timing: Sample >2 weeks post-surgery for clearance

Clinical Decision Support

Author

AI Group - Biomedical AI Platform