解释性人工智能对omics数据:一个系统的映射研究研究
Philipp A Toussaint1,2, Florian Leiser1, Scott Thiebes1
1Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Briefings in bioinformatics
|December 19, 2023
概括
可解释的人工智能 (XAI) 对于分析omics数据至关重要. 这项研究绘制了XAI在奥米克应用的地图,确定了趋势,研究差距以及生物见解和临床实践的未来方向.
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
- 基因组学就是基因组学.
背景情况:
- 可解释的人工智能 (XAI) 越来越多地用于omics数据分析.
- 跨学科的性质导致了碎片化的发现,需要一个整合的概述.
- 需要进行系统的映射研究来综合目前关于XAI的研究.
研究的目的:
- 为了提供一个全面的概述XAI应用在omics数据分析.
- 确定常用的AI和可解释性方法.
- 突出研究差距,并建议未来的研究方向.
主要方法:
- 系统地绘制科学文献的研究.
- 搜索了Scopus,PubMed,科学网,BioRxiv,MedRxiv,以及arXiv. 这些网站的搜索结果.
- 为人工智能方法,可解释性和omics数据开发了一个具有10个方面的编码方案.
主要成果:
- 包括2010-2023年间发表的405篇论文.
- 使用各种人工智能方法分析了基因组,转录组,蛋白质组和代谢组数据.
- 确定了特征相关性和视觉解释作为常见的临时后方法,以及可解释方法的透明模型.
结论:
- 在XAI中,对于omics数据存在显著的研究缺口.
- 提出了八个研究方向,以推动该领域的发展.
- 对于XAI在奥米克研究中的临床采用,仍然存在挑战.
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