Article
A Contrastive Learning Framework for Efficient Viral Escape Prediction.
IEEE transactions on computational biology and bioinformatics - 1 Jan 2026
Tenekeci Samet, Sezgin Efe, Tekir Selma
Abstract excerpt
Understanding the complex rules and mechanisms behind viral evolution is crucial for developing better preventive treatments, yet predicting immune-evading mutations remains challenging. Recent advances in protein language models have led to novel approaches for in silico analysis of viral escape. We introduce CoV-SNN, unifying variant classification and escape prediction within an efficient contrastive learning...
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