可解释的生物信息学人工智能:方法,工具和应用
Md Rezaul Karim1,2, Tanhim Islam3, Md Shajalal4
1Computer Science 5 - Information Systems and Databases, RWTH Aachen University, Germany.
Briefings in bioinformatics
|July 21, 2023
概括
可解释的人工智能 (XAI) 通过适应域异的机器学习 (ML) 方法来提高生物信息学的透明度. 这种方法解决了复杂的人工智能的黑盒性质,提高了关键应用程序的决策公平性和问责制.
科学领域:
- 生物信息学是一种生物信息学.
- 生物医学信息学 生物医学信息学
- 精准医学是一门精准的医学.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 生物信息学中复杂的AI和机器学习 (ML) 模型往往是不透明的"黑子"系统,阻碍了对其决策过程的理解.
- 人工智能缺乏透明度给最终用户,决策者和开发者带来了挑战,特别是在可解释性和问责制至关重要的医疗保健领域.
- 算法公平性是一个日益关注的问题,要求人工智能决策不受特定群体或个人偏见的影响.
研究的目的:
- 在生物信息学领域讨论可解释性和算法透明度的重要性.
- 审查现有的模型特定和模型不可知可解释的ML方法和工具,强调它们对生物信息学的局限性.
- 通过案例研究,展示可解释AI (XAI) 在生物信息学中的应用和好处.
主要方法:
- 概述域异的可解释的ML方法和工具.
- 讨论为生物信息学研究定制和调整现有的可解释的ML方法.
- 生物成像,癌症基因组学和文本挖掘的案例研究,以说明XAI应用.
主要成果:
- 现有的可解释的ML方法通常需要定制,以便在生物信息学中有效应用.
- 可以成功地适应XAI方法来提高生物信息学任务的透明度和公平性.
- 案例研究表明,XAI在生物成像,癌症基因组学和文本挖掘方面的实用好处.
结论:
- 可解释人工智能 (XAI) 对于提高生物信息应用的透明度,问责制和公平性至关重要.
- 现有的可解释的ML方法的定制是它们在生物信息学中成功实施的关键.
- 本综述是旨在提高人工智能可解释性和生物信息学决策透明度的研究人员的起点.
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