大型语言模型的比较性能评估,用于提取分子相互作用和途径知识
Gilchan Park1, Byung-Jun Yoon1, Xihaier Luo1
1Computing and Data Sciences, Brookhaven National Laboratory, Upton, New York, USA.
概括
大型语言模型 (LLM) 可以自动提取基因组规模的分子相互作用数据,改善生物知识的发现. 较大的LLM在识别复杂的基因和蛋白质相互作用方面表现有前途,尽管在识别多样化的基因组方面仍然存在挑战.
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
- 基因组学就是基因组学.
背景情况:
- 了解生物分子相互作用对于破译生物系统至关重要.
- 分子相互作用数据的专家策划是耗时和劳动密集的.
研究的目的:
- 探索使用大型语言模型 (LLM) 来自动化基因组规模的分子相互作用知识提取.
- 评估LLM在诸如蛋白质-蛋白质相互作用识别和基因调控关系推断等任务上的表现.
主要方法:
- 对生物任务的各种LLM的评估.
- 在识别蛋白质-蛋白质相互作用,辐射受影响的基因和基因调节关系方面的LLM性能分析.
- 与已建立的分子相互作用和通路数据库进行比较.
主要成果:
- 较大的LLM通常在提取复杂的基因和蛋白质相互作用方面表现更好.
- 在识别相关的生物分子和预测它们的相互作用方面,LLM显示出潜力.
- 在识别功能多样化的基因组和高度相关的调节关系方面存在挑战.
结论:
- LLM为人工智能驱动的生物知识发现提供了一个有前途的途径.
- 自动提取分子相互作用数据可以显著加速研究.
- 需要进一步发展,以解决目前在生物数据分析中的LLM限制.
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