在Reactome策划过程中使用ChatGPT
Krishna Tiwari1,2, Lisa Matthews3, Bruce May4
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
bioRxiv : the preprint server for biology
|November 21, 2023
概括
像ChatGPT这样的生成人工智能,在Reactome策划中显示出对自动化生物途径数据收集和注释的承诺. 这项研究探讨了它的效率和挑战,旨在改善蛋白质到通路的关联.
科学领域:
- 生物信息学是一种生物信息学.
- 人工智能在生物学中的应用
- 路径策划策划 路径策划
背景情况:
- 包括ChatGPT在内的生成AI正在迅速发展.
- 在生物路径策划中手动收集和注释数据是劳动密集的.
- 反应体是通路信息的重要资源.
研究的目的:
- 探索ChatGPT在Reactome的数据收集和注释中的实用性.
- 开发一个自动化或半自动化框架来收集路径信息.
- 为了提高效率和准确性,将ChatGPT生成的注释与手动策划进行比较.
主要方法:
- 使用ChatGPT/GPT4进行路径注释和丰富,并与手动策划并行.
- 对Reactome数据采用了以反应为中心的方法.
- 对人工智能生成的信息与手动提取的信息进行了比较分析.
- 确定并描述了改进人工智能输出的方法.
主要成果:
- 聊天GPT在解决注释差距方面展示了有希望的功能.
- 识别了使用人工智能用于Reactome策划的固有挑战.
- 开发了改进人工智能输出的准确性和详细性的方法.
- 促进了对人工智能辅助与手动策划的比较评估.
结论:
- 生成型人工智能,特别是ChatGPT,提供了简化Reactome策划的潜力.
- 半自动化框架可以提高效率和蛋白质与路径的关联.
- 为了在生物数据策划中强大的人工智能集成,需要进一步的改进和验证.
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