Jie Chen

Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution

This paper develops a deep-learning framework to predict real-world prevalent SARS-CoV-2 mutations using in silico virus evolution and protein language models, achieving accurate predictions of future dominant mutations.

E2VD: a unified evolution-driven framework for virus variation drivers prediction

E2VD predicts virus variation drivers using an evolution-driven framework, outperforming existing methods and identifying critical mutations for viral survival and risk.

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

This paper develops an AI-based simulation method to predict future high-risk SARS-CoV-2 variants by simulating RBD mutations and screening potential variants efficiently.