生物信息学和生物医学信息学与ChatGPT:第一年回顾
Jinge Wang1, Zien Cheng1, Qiuming Yao2
1Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, West Virginia, USA.
Quantitative biology (Beijing, China)
|October 4, 2024
概括
在2023年,像ChatGPT这样的大型语言模型聊天机器人在生物信息学中的使用增加了. 本调查审查了ChatGPT应用程序,突出了其在现场的优势和局限性.
科学领域:
- 生物信息学和生物医学信息学
背景情况:
- 大型语言模型 (LLM) 和聊天机器人,特别是聊天生成预训练变压器 (ChatGPT),在2023年获得了突出地位.
- 这些先进的人工智能工具在科学学科中的应用已经出现了显著的激增.
研究的目的:
- 在2023年全面调查ChatGPT在生物信息学和生物医学信息学中的应用.
- 确定ChatGPT在这个科学领域的当前能力和限制.
- 提供对未来人工智能在生物信息学中的研究和开发方向的见解.
主要方法:
- 从2023年开始对文学和研究出版物的系统审查,重点关注生物信息学中的ChatGPT应用.
- 在关键领域的应用程序分类,包括奥米克,遗传学,文本挖掘,药物发现,图像分析,编程和教育.
- 分析报告中的成功,挑战和在实施过程中遇到的局限性.
主要成果:
- 聊天GPT已经在各种生物信息学领域进行了探索:奥米克数据分析,遗传变异解释,生物医学文本挖掘,加速药物发现,帮助生物医学图像理解,协助生物信息学编程任务,并加强生物信息学教育.
- 确定的优势包括用于基于文本的任务的自然语言处理能力和代码生成帮助.
- 关键的限制包括潜在的不准确性,数据隐私问题以及需要专家验证.
结论:
- 聊天GPT为生物信息学和生物医学信息学提供了一个强大的,尽管新兴的工具.
- 需要进一步的研究来完善其准确性,解决伦理方面的考虑,并将其更牢固地纳入研究工作流程.
- 人工智能,特别是LLM,对生物信息学进行革命的潜力是巨大的,需要继续探索和开发.
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