在生成性人工智能时代的生物数据库
Mihai Pop1, Teresa K Attwood2, Judith A Blake3
1Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States.
Bioinformatics advances
|April 3, 2025
概括
公共生物数据库中的错误浪费资源,阻碍研究. 改善数据工程教育,研究错误传播和资助数据库维护对于可靠的科学数据至关重要.
科学领域:
- 生物数据科学是生物数据科学.
- 生物信息学是一种生物信息学.
- 生命科学中的数据管理
背景情况:
- 公共生物数据库对于现代研究至关重要.
- 数据错误及其传播导致资源浪费和科学发现不可靠.
- 生成型人工智能可能会加剧数据完整性问题,因为它可以轻松生成合成数据.
研究的目的:
- 概述生物数据生态系统中的关键问题.
- 推减少数据错误及其传播的策略.
- 突出需要改进数据工程教育和研究的需要.
主要方法:
- 审查生物数据管理当前的挑战.
- 确定改善数据质量和来源的关键领域.
- 为研究人员,机构和资助机构制定建议.
主要成果:
- 在生物数据生态系统中,存在关于错误引入和传播的重大问题.
- 生成型人工智能对数据完整性构成新的威胁.
- 教育计划,数据来源研究和数据库管理至关重要.
结论:
- 对生命科学家来说,加强数据工程教育至关重要.
- 需要对数据来源和错误传播进行进一步的研究.
- 建议增加对公共生物数据库维护和管理的资金.
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