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Dataset for Unveiling Viral Escape Mechanisms with Machine Learning: A Transformative Approach to Mutation Analysis for SARS-CoV-2 and Beyond

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Persistent viruses like influenza, HIV, Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments.  This study leverages a Long Short-Term Memory (LSTM) based deep learning architecture to analyze an extensive dataset of over 3.1 million unique viral spike protein sequences, with SARS-CoV-2 serving as the primary example. Our model, Escape Elite Network(EEN) outperforms existing methods in detecting escape mutations across diverse datasets.

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