A zero-parameter first-principles gate framework for full-length TP53 missense variant interpretation (opens in new tab)

Author summary Most tools that predict whether a TP53 mutation is disease-causing rely on machine learning trained on large datasets. These models can be accurate but typically cannot explain why a particular mutation is flagged — which specific physical rule is broken. I developed a framework called Gate & Channel that takes a different approach: instead of learning patterns from data, it encodes explicit physical rules, such as “this mutation destroys a zinc-binding site.” If any single rul...