面对数据洪水:人工智能如何用于研究植物压力
Eugene Koh1, Rohan Shawn Sunil1, Hilbert Yuen In Lam1
1School of Biological Scie nces, Nanyang Technological University, Singapore, Singapore.
Computational and structural biotechnology journal
|October 17, 2024
概括
先进的机器学习 (ML) 和人工智能 (AI) 模型为分析植物应力弹性数据提供了强大的工具. 本综述探讨了植物科学中的ML/AI应用,强调了它们在释放基因组洞察力方面的潜力.
科学领域:
- 植物科学 植物科学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 基因组学时代为植物应激弹性研究产生了庞大的,异质的高吞吐量数据.
- 分析这些复杂的数据需要先进的计算方法.
- 目前的深度学习模型擅长基于序列的预测,但在理解基因调节方面还处于新生阶段.
研究的目的:
- 对植物应激反应的数据资源,生物信息工具和ML/AI应用进行审查.
- 在植物压力研究中使用大型语言模型 (LLM) 来创建知识图表.
- 促进跨学科的合作,以获得关于植物弹性的数据驱动的见解.
主要方法:
- 对植物压力研究中的ML/AI现有文献的审查.
- 数据资源和生物信息工具的探索.
- 展示一个公开可用的LLM (ChatGPT) 用于知识图表生成.
主要成果:
- 深度学习模型在预测基因结构,功能和表达方面表现有前途.
- 在高通量现象学和理解植物应激反应方面,ML/AI存在显著的潜力.
- 法学士申请成功生成了实验和计算方法的知识图.
结论:
- 机器学习/人工智能模型对于将大型生物数据集提炼成可操作的知识至关重要.
- 需要进一步开发和应用这些模型,以推进植物应激弹性研究.
- 跨学科的合作对于充分利用植物科学多学科数据的全部潜力至关重要.
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