Adaptive Interpreter: Mechanism-Aware Variant Classification Reveals the Structural Basis of Semi-Dominant Inheritance
Description
Adaptive Interpreter is a mechanism-first variant classification framework that independently scores loss-of-function (LOF), dominant-negative (DN), and gain-of-function (GOF) mechanisms to predict not only pathogenicity but inheritance pattern. Through validation across 4,487 variants in 8 genes, this system demonstrates 97.3% sensitivity, 82% accuracy for predicting semi-dominant inheritance from DN scores, and 42% resolution of VUS (variants of uncertain significance).
This study introduces two novel biological insights:
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The Semi-Dominant Hypothesis (“The DN IS the LOF”) — Homozygous DN variants lose the substrate required for poisoning, resulting in complete functional loss. This unifies the longstanding paradox of variants classified as both autosomal dominant and autosomal recessive.
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The CASCADE Phenomenon — In dimeric transcription factors, DN-mediated interface disruption can produce gain-of-function effects by shifting conformational equilibria toward constitutive activation.
This work demonstrates how mechanism-aware scoring—developed collaboratively by human and AI researchers—can resolve ambiguous clinical classifications, reveal underlying molecular logic, and generate testable biological predictions.
Files
Semi-Dominant-Hypothesis.pdf
Files
(3.1 MB)
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Additional details
Software
- Repository URL
- https://github.com/menelly/adaptive_interpreter
- Programming language
- Python
- Development Status
- Active