生物信息学和计算生物学中的扩散模型
Zhiye Guo1,2, Jian Liu1,2, Yanli Wang1,2
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
Nature reviews bioengineering
|April 5, 2024
概括
否认扩散模型,一种生成性AI,正在改变生物信息学. 本次审查涵盖了它们的框架,在蛋白质和药物设计等领域的应用以及未来的潜力.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 排斥扩散模型是先进的生成人工智能技术.
- 这些模型在计算机视觉,NLP和生物信息学中具有广泛的应用.
研究的目的:
- 审查三个扩散建模框架的核心概念和理论基础.
- 探索扩散模型在生物信息学和计算生物学中的多样化应用.
- 突出现有的开源工具,并讨论未来的方向.
主要方法:
- 介绍无声扩散的概率模型.
- 噪音条件下评分网络的解释.
- 评分随机微分方程的概述.
主要成果:
- 扩散模型在蛋白质设计和生成方面显示出显著的前景.
- 应用范围扩展到药物和小分子设计.
- 在蛋白质 - 配体相互作用建模和冷电子显微镜图像分析中有效使用.
- 在单细胞数据分析中的实用性.
结论:
- 扩散模型为生物数据分析和生成提供了强大的新方法.
- 开源工具正在加速该领域的采用和研究.
- 生物信息学的未来应用是巨大的和变革性的.
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