A Contrastive Learning Framework for Efficient Viral Escape Prediction
This project is conducted under the leadership of Assoc. Prof. Selma Tekir of Computer Engineering department. This study developed the CoV-SNN, unifying variant classification and escape prediction within an efficient contrastive learning framework. CoV-SNN, built on a Siamese neural network architecture, classifies previously unseen variants by modeling sequence-level similarities and differences through embeddings from CoV-RoBERTa, a lightweight protein language model trained on high-quality SARS-CoV-2 Spike sequences. It prioritizes escape sequences using an enhanced Constrained Semantic Change Search (CSCS) function that maps antigenic variation to semantic change and viral fitness to sequence probability.
