Chenchen Qin

This paper introduces USM-AA, an alignment-aware genomic foundation model that improves long-range DNA modeling by incorporating reference genome coordinates and reverse-complement masked learning.

The paper develops a large language model to predict SARS-CoV-2-specific TCRs and shows their correlation with antibody levels post-vaccination.

The paper introduces tFold-TCR, a model for predicting TCR-pMHC structures, achieving higher accuracy and speed than existing methods and creating a large database of these structures.

CD-GPT is a foundation model that captures sequence relationships between DNA, RNA, and proteins, performing well in predictive and generative tasks across molecular types.

This paper introduces tFold-Ab and tFold-Ag for fast and accurate prediction of antibody and antibody-antigen complex structures, showing improved speed and performance over AlphaFold-Multimer.

DNAGPT pre-trains a modified generative pre-trained transformer (GPT) architecture on over 200 billion mammalian base pairs to jointly process sequence and numerical data across diverse DNA analysis tasks.