揭开黑盒子的神秘面:对生物信息学中可解释的人工智能 (XAI) 的调查
Aishwarya Budhkar1, Qianqian Song2, Jing Su3
1Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 700 N Woodlawn Ave, Bloomington, IN 47408, USA.
Computational and structural biotechnology journal
|February 3, 2025
概括
可解释人工智能 (XAI) 对于生物信息学中的透明决策至关重要. 本综述分析了XAI对omics和成像数据的技术,解决用户需求并提供开发指南.
科学领域:
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 黑子AI/ML模型缺乏透明度,阻碍了用户的信心.
- 可解释性AI (XAI) 和可解释性技术正在出现以解决这一问题.
- 生物信息学越来越多地利用AI/ML,需要可解释的模型.
研究的目的:
- 审查生物信息学中现有的XAI技术,重点关注omics和成像数据.
- 分析生物信息学中对XAI的需求.
- 识别当前的XAI方法,它们的局限性和用户特定需求.
主要方法:
- 对生物信息学中可解释性技术的文献综述.
- 专注于omics和成像数据分析中的应用.
- 分析用户要求和系统开发者的需求.
主要成果:
- 在生物信息学中对XAI的需求很大,这是由于透明度需求.
- 识别当前的XAI方法及其在omics和成像方面的局限性.
- 强调将XAI定制为特定的生物信息应用和用户群体.
结论:
- 在生物信息学中,XAI对于可信的人工智能至关重要.
- 对奥米克和成像数据需要进一步开发XAI方法.
- 为开发人员提供了指导方针,以创建有效的XAI系统.
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