超越模型:数据基础设施作为自主虚拟实验室的基础.
Lea M Sommer1, Teddy Groves1, Alberto Santos1
1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs Lyngby, Denmark.
Current opinion in biotechnology
|January 14, 2026
概括
人工智能 (AI) 和机器学习正在改变生物技术,但需要更好的数据基础设施. 以质量,标准化和互操作性为重点的以数据为中心的方法对于在生物制造中推进AI至关重要.
科学领域:
- 生物技术是生物技术.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 人工智能和机器学习正在改变生物技术.
- 目前的影响受到数据基础设施不足的限制,数据质量,标准化和互操作性是关键瓶.
研究的目的:
- 审查人工智能支持生物制造当前数据基础设施的局限性.
- 倡导以数据为中心的方法来克服这些挑战并推动该领域的发展.
主要方法:
- 对人工智能和生物制造当前文献和实践的审查.
- 在设计-建造-测试-学习周期中分析瓶.
- 识别人工智能发展的基本数据实践.
主要成果:
- 数据质量,标准化和互操作性是关键的限制.
- 手动测试到学习的数据摄入步骤引入了显著的延迟.
- 精心策划的存储库,一致的元数据,实时验证和高效的学习策略至关重要.
结论:
- 为了实现人工智能支持的生物制造,需要以数据为中心的战略.
- 解决数据限制将使可扩展,可靠和自主虚拟实验室成为可能.
- 坚持可查找,可访问,可互操作,可重复使用 (FAIR) 原则至关重要.
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