DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis
  Tasks
The success of the GPT series proves that GPT can extract general information
from sequences, thereby benefiting all downstream tasks. This motivates us to
use pre-trained models to explore the hidden information in DNA sequences.
However, data and task requirements in DNA sequence analysis are complexity and
diversity as DNA relevant data includes different types of information, such as
sequences, expression levels, etc, while there is currently no model
specifically designed for these characteristics. Hereby, we present DNAGPT, a
generalized foundation model pre-trained on over 10 billion base pairs from 9
species which can be fine-tuned for any DNA sequence analysis task. Our model
can simultaneously process or output DNA sequences and numbers. In addition,
our unique token design allows users to design prompts according to their own
task requirements, making it applicable to any type of task. We have evaluated
our model on classification, regression, and generation tasks. We demonstrate
that DNAGPT benefits from pre-training, and therefore can bring performance
gains to any downstream task. Our model is not only a new attempt in the field
of genomes analysis, but also provides a new direction for the application of
foundation models in biology.
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Daoan Zhang
claimed_verified
2024-10-30T09:57:14.328Z
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/avatars/1f9f0db4eee887f9ed03dffc8c36ea23.svg
DaoanZhang
user
DwanZhang
64af7ecde1a1a94474dcbc34
Weitong Zhang
64af7ecde1a1a94474dcbc35
Bing He
64af7ecde1a1a94474dcbc36
Jianguo Zhang
64af7ecde1a1a94474dcbc37
Chenchen Qin
64af7ecde1a1a94474dcbc38
Jianhua Yao
DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis
  Tasks
The success of the GPT series proves that GPT can extract general information
from sequences, thereby benefiting all downstream tasks. This motivates us to
use pre-trained models to explore the hidden information in DNA sequences.
However, data and task requirements in DNA sequence analysis are complexity and
diversity as DNA relevant data includes different types of information, such as
sequences, expression levels, etc, while there is currently no model
specifically designed for these characteristics. Hereby, we present DNAGPT, a
generalized foundation model pre-trained on over 10 billion base pairs from 9
species which can be fine-tuned for any DNA sequence analysis task. Our model
can simultaneously process or output DNA sequences and numbers. In addition,
our unique token design allows users to design prompts according to their own
task requirements, making it applicable to any type of task. We have evaluated
our model on classification, regression, and generation tasks. We demonstrate
that DNAGPT benefits from pre-training, and therefore can bring performance
gains to any downstream task. Our model is not only a new attempt in the field
of genomes analysis, but also provides a new direction for the application of
foundation models in biology.
GPT pre-trained models DNA sequences DNAGPT foundation model base pairs token design prompts classification regression generation tasks pre-training