DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence
  Analysis Tasks
Authors: name: Daoan Zhang, name: Weitong Zhang, name: Bing He, name: Jianguo Zhang, name: Chenchen Qin, name: Jianhua Yao

Pre-trained large language models demonstrate potential in extracting
information from DNA sequences, yet adapting to a variety of tasks and data
modalities remains a challenge. To address this, we propose DNAGPT, a
generalized DNA pre-training model trained on over 200 billion base pairs from
all mammals. By enhancing the classic GPT model with a binary classification
task (DNA sequence order), a numerical regression task (guanine-cytosine
content prediction), and a comprehensive token language, DNAGPT can handle
versatile DNA analysis tasks while processing both sequence and numerical data.
Our evaluation of genomic signal and region recognition, mRNA abundance
regression, and artificial genomes generation tasks demonstrates DNAGPT's
superior performance compared to existing models designed for specific
downstream tasks, benefiting from pre-training using the newly designed model
structure.