通过整合来自大型语言模型的临床特征来进行多omics集群
Xiucai Ye1, Tianyi Shi1, Dong Huang1
1Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
Methods (San Diego, Calif.)
|April 3, 2025
概括
这项研究引入了一种新的癌症亚型分类方法,使用多omics集群,结合大语言模型 (LLMs) 提取的临床数据. 这种方法通过将各种omics数据与关键的临床背景相结合,提高了癌症亚型的准确性.
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:
- 多omics聚类整合了各种omics数据用于生物系统分析和癌症亚型.
- 当前的方法往往忽视了在非结构化的医学文本中存在的有价值的临床背景.
研究的目的:
- 为多主题集群开发一种新的框架,该框架整合了通过大型语言模型 (LLM) 提取的临床特征.
- 通过以结构化临床信息丰富omics数据来增强癌症亚型.
主要方法:
- 利用基于BERT的模型从病理学报告中提取临床特征,将非结构化文本转换为结构化数据.
- 集成提取的临床特征与omics数据使用自动编码器来丰富omics层.
- 采用单数值分解 (SVD) 进行潜子空间投影,然后进行光谱聚类以获得最终结果.
主要成果:
- 拟议的框架在多omics聚类中表现出优异的性能,用于在六个数据集和三个omics级别的癌症亚型.
- 与现有方法相比,LLM衍生的临床特征的整合显著改善了聚类性能.
- 对几个最先进的方法验证了框架的有效性.
结论:
- 纳入从LLM中获得的临床背景对于推进多omics分析至关重要.
- 拟议的框架为精准医学提供了一个强大的工具,通过增强的多omics集群来改进癌症亚型.
- 强调LLMs在生物医学研究和临床应用中的变革潜力.
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