TransGRN:一种基于转移学习的框架,用于推断跨细胞系的基因调节网络
IEEE journal of biomedical and health informatics
|November 5, 2025
概括
通过转移学习,TransGRN推断基因调节网络 (GRNs),在有限的数据场景中提高准确性. 这种方法利用跨细胞系数据和大型语言模型进行强大的基因调控网络分析.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 基因调节网络 (GRNs) 对于细胞功能至关重要.
- 单细胞RNA测序 (scRNA-seq) 可实现高分辨率的GRN推断.
- 由于依赖于先前信息,现有的方法在有限的数据 (少数射击学习) 上扎.
研究的目的:
- 开发一种新的计算方法来推断跨细胞系的GRNs,特别是在数据稀缺的情况下.
- 为了克服现有的GRN推断方法在少数镜头设置中的局限性.
- 通过转移学习提高GRN推断的准确性和适用性.
主要方法:
- 提出了TransGRN,一种基于转移学习的方法,用于GRN推断.
- 利用来自多个来源的scRNA-seq数据进行跨细胞系预训练策略.
- 来自大型语言模型的综合生物知识.
- 开发了一个调控相互作用提取模块,将基因表达和语义信息结合起来.
主要成果:
- 在基准测试中,TransGRN展示了最先进的性能.
- 在几次射击的GRN推理任务中取得了优异的结果.
- 在细胞系中成功转移了可概括的基因-基因调节模式.
结论:
- 通过有限的数据,TransGRN有效地解决了GRN推断的挑战.
- 该方法通过利用跨细胞系学习和大型语言模型来增强GRN分析.
- 为了解不同细胞类型的细胞机制提供了强大的工具.
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