Forensic DNA Mixture Analysis AI — Automated NOC Estimation & Quality ScoreCard
AI-powered forensic DNA mixture triage software. Automated number-of-contributors (NOC) estimation, explainable quality scoring, and seamless integration with STRmix and EuroForMix probabilistic genotyping.
The Problem: Garbage In — Garbage Out
INPUT: Subjective Ambiguity
Traditional subjective analysis of electropherograms often leads to expert error in determining the number of contributors (NOC) and distinguishing artifacts from true alleles.
- Messy, uncertain baseline noise
- Stutter or artifact confusion
- Subjective "eye-balling" of complex mixtures
OUTPUT: Calculation Failure
When bad data enters probabilistic genotyping software, the computational output is compromised.
- ERROR: Invalid LR Calculation. Resource Wasted.
- DATA CORRUPTION
- NOC MISMATCH
- CALCULATION ABORTED
Technological Architecture: A Hybrid Approach
We replace the 'Black Box' with a 'Glass Box'.
TIER A: Deterministic Foundation
Math before AI. Cleaning the signal.
- Signal Morphology: Gaussian distribution approximation filters electronic noise.
- Feature Extraction: Rigid calculation of Peak Height Ratio (PHR) and allelic coverage.
- Stutter Filtering: Rule-based detection of n-1 / n+1 threshold violations.
Tier A cleans data using rigid signal processing mathematics.
TIER B: The Brain
Intelligence and Contextual Analysis
- 1D-CNN: Recognizes local defect patterns and morphology (Allele, Stutter, Noise).
- LSTM / Transformers: Analyzes global imbalance across the entire sequence.
- Output: Instant categorization into 1, 2, 3, 4+ Contributors or Trash.
Tier B applies deep learning only to the sanitized features.
The Product: Quality ScoreCard & Explainability
Locus D3S1358: 7 peaks detected.
Global PHR Variance: 0.22 (Exceeds 2-person threshold).
Not just a verdict, but a justified scientific opinion.
Full audit trail for courtroom admissibility. Overcoming the 'Black Box' dilemma by meeting legal requirements for explainability (XAI) and traceability (Zhang 2025).
Quality Check: No degradation detected. Degradation Index: 1.01 (Normal Range: 0.8 - 1.2)
Integration Workflow & Hardware Power
The Intelligent Router
We do not replace your PGS; we optimize it. Automating the setup allows experts to focus on final review.
- Reject: Generates report on 'Trash' data, saving computation.
- Single Source: Fast-tracks clean single contributor profiles.
- Complex Mixture: Auto-configures for EuroForMix/STRmix.
The Cerebras Advantage
The Central Sorting Brain for National Labs.
- Cerebras Wafer Scale Engine
- 44 GB On-Chip SRAM
- Zero Latency & Industrial Throughput
- Parallel Inference (20+ models)
Operational Impact & Summary
Objectivity
Removes subjective 'eye-balling'. Standardized, repeatable measurement.
Cost Efficiency
Rejects 'Trash' before processing. Saves reagent and compute costs.
Legal Integrity
Daubert compliant. Explainable AI (XAI) withstands cross-examination.
FAQ
Kan AI nauwkeurig het aantal bijdragers (NOC) bepalen in complexe DNA-mengsels?
Ja, onze AI schat het NOC nauwkeurig door de signaalmorfologie en globale onevenwichtigheid te analyseren. Het filtert elektronische ruis en stutterpieken deterministisch uit voordat diepe leerprocessen worden toegepast, waardoor klinische precisie wordt gegarandeerd.
Hoe voorkomt deze forensische software probabilistische genotypingsfouten?
Het voorkomt fouten door 'afval'gegevens te verwijderen voordat de berekening plaatsvindt. Door de NOC-schatting te automatiseren en een deterministische kwaliteitsscorekaart te bieden, voorkomt het subjectieve expertfouten die downstreamtools zoals STRmix of EuroForMix aantasten.
Is de AI-analyse toelaatbaar in de rechtszaal volgens de Daubert-normen?
Ja, het systeem is volledig Daubert-conform. Het biedt Explainable AI (XAI) met een complete audittrail en deterministische rechtvaardigingen voor elke voorspelling, waardoor juridische integriteit en traceerbaarheid tijdens tegenonderzoek worden gewaarborgd.