迈向机器公平:代表软件和数据集,以促进机器的重复使用和科学发现
Michael M Wagner1, William R Hogan2, John D Levander3
1Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA.
Journal of biomedical informatics
|May 1, 2024
概括
评估一种用于编写传染病流行病学研究对象的新型机器 (M1),揭示了语义约束方面的挑战. 软件必须以更简单的输入进行设计,以实现自动发现和加速科学研究.
科学领域:
- 传染病流行病学 传染病流行病学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 研究对象组成的自动发现可以加速科学发现.
- 目前的方法缺乏可扩展的解决方案来获取有关软件输入语义约束的知识.
研究的目的:
- 用传染病流行病学数据评估一种用于编写数字研究对象的新型机器 (M1).
- 为了确定M1的自动化作文能力所需的语义约束.
主要方法:
- 使用详尽的搜索实现了一个仅与M1 (M1DFM) 相匹配的数据格式.
- 在一系列传染病流行病学软件,数据集和数据格式上测试了M1DFM.
- 进行错误分析以确定必要的语义约束.
主要成果:
- 在识别有效组合时,M1DFM实现了61.7%的精度.
- 错误分析强调了需要语义约束和改进数据服务处理的需要.
- 复杂的数据格式对表示语义约束构成挑战,需要更简单的软件输入.
结论:
- 算法组合具有加速科学发现的潜力,但需要解决语义约束的知识获取问题.
- 软件设计必须考虑语义约束表示,类似于简单服务API.
- M1-FAIR原则指导可复合性,以提高重复使用和发现.
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