生物医学中的变压器模型
Sumit Madan1,2, Manuel Lentzen3,4, Johannes Brandt5
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany. sumit.madan@scai.fraunhofer.de.
变压器模型是一种深度神经网络,在AI中越来越多地用于分析各种生物医学数据. 本综述涵盖了它们的应用,可解释性以及该领域未来的研究方向.
科学领域:
- 人工智能的人工智能
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 深度神经网络 (DNN) 已经改变了人工智能 (AI).
- 变压器模型,最初是用于自然语言处理,现在在生物序列和电子健康记录等序列数据方面表现出色.
- 生物医学变压器模型 (例如,BioBERT,MedBERT) 正在为科学调查而出现.
研究的目的:
- 审查变压器模型在分析生物医学数据中的开发和应用.
- 探索可解释的人工智能 (XAI) 策略,以实现变压器模型的可解释性.
- 讨论生物医学中变压器模型的当前局限性和未来研究途径.
主要方法:
- 审查变压器模型架构及其对生物医学数据的适应.
- 分析各种生物医学数据集中的应用:文本,蛋白质序列,电子健康记录,图像和图表.
- 对基于变压器的生物医学模型应用的可解释的人工智能技术的检查.
主要成果:
- 变压器在处理各种生物医学数据类型方面具有显著的实用性.
- 像BioBERT和MedBERT这样的特定模型在生物医学问题解答和分析方面显示出希望.
- 可解释的AI方法对于理解和信任这些复杂模型至关重要.
结论:
- 变压器模型是推动生物医学研究的强大工具.
- 需要进一步开发以解决局限性并提高模型的可解释性.
- 未来的方向包括人工智能在生物医学中的新应用和改进的可解释性技术.
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