DOME注册表:实施社区范围的建议,报告生物学中的监督机器学习
Omar Abdelghani Attafi1, Damiano Clementel1, Konstantinos Kyritsis2
1Department of Biomedical Sciences, University of Padova, Padova 35131, Italy.
GigaScience
|December 11, 2024
概括
在生物学中的监督机器学习 (ML) 需要更好的验证. 数据优化模型评估 (DOME) 注册表标准化了ML研究报告,通过精心策划的数据库和评分系统提高了透明度和可重复性.
科学领域:
- 生命科学 生命科学
- 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:
- 监督机器学习 (ML) 在生物研究中越来越普遍.
- 现有的ML研究往往缺乏标准化的验证和透明的报告,阻碍了可重现性.
- 数据优化模型评估 (DOME) 倡议提供了加强ML研究严格性的建议.
研究的目的:
- 引入DOME注册表,这是一个中央数据库,用于管理和访问已发表的ML研究的DOME相关信息.
- 促进生命科学中ML方法的透明和可重现的报告.
- 通过独特标识符和DOME分数来促进对ML方法的标准化评估.
主要方法:
- 开发DOME注册表 (registry.dome-ml.org) 作为ML研究文档的数据库.
- 与外部资源 (ORCID,APICURON,数据管理向导) 的集成,以简化注释.
- 将唯一标识符和DOME分数分配给注册表中的出版物.
主要成果:
- 建立一个功能性的DOME注册表,用于ML研究的全面文档.
- 使用集成的外部资源演示精简的注释流程.
- 实施DOME评分系统,用于对ML出版物的标准化评估.
结论:
- DOME注册表为提高生命科学中的ML透明度和可重复性提供了宝贵的资源.
- 社区策划和出版商采用DOME标准对于未来的增长和影响至关重要.
- 持续完善DOME分数定义将进一步加强ML方法评估的标准化.
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