title: DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis
    Tasks
summary: 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.
paper_authors._id: 64af7ecde1a1a94474dcbc33
paper_authors.name: Daoan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:57:14.328Z
paper_authors._id: 64ee52837ce493905c82bed5
paper_authors.avatarUrl: /avatars/1f9f0db4eee887f9ed03dffc8c36ea23.svg
paper_authors.fullname: DaoanZhang
paper_authors.type: user
paper_authors.user: DwanZhang
paper_authors._id: 64af7ecde1a1a94474dcbc34
paper_authors.name: Weitong Zhang
paper_authors._id: 64af7ecde1a1a94474dcbc35
paper_authors.name: Bing He
paper_authors._id: 64af7ecde1a1a94474dcbc36
paper_authors.name: Jianguo Zhang
paper_authors._id: 64af7ecde1a1a94474dcbc37
paper_authors.name: Chenchen Qin
paper_authors._id: 64af7ecde1a1a94474dcbc38
paper_authors.name: Jianhua Yao
paper_title: DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis
    Tasks
paper_summary: 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.
paper_ai_keywords: GPT pre-trained models DNA sequences DNAGPT foundation model base pairs token design prompts classification regression generation tasks pre-training