癌症基因识别通过整合因果提示大型语言模型与omics数据驱动的因果推理
Haolong Zeng1, Chaoyi Yin1, Chunyang Chai1
1School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, Changchun 130012, Jilin Province, China.
Briefings in bioinformatics
|March 12, 2025
概括
这项研究引入了一种新的框架,即整合性因果基因识别 (ICGI),使用大型语言模型和因果推理准确识别癌症相关基因. 该方法改善了对癌症机制的理解,并有助于开发治疗策略.
科学领域:
- 基因组学和计算生物学
- 癌症研究 癌症研究
- 人工智能在医学中的应用
背景情况:
- 识别与癌症相关的基因对于理解癌症机制和开发向疗法至关重要.
- 传统的基因鉴定方法由于混因素和方法限制,往往会产生偏见和难以解释的结果.
研究的目的:
- 开发一个新的框架,整合性因果基因识别 (ICGI),用于识别跨多个omics域的癌症基因.
- 利用大型语言模型 (LLM) 和因果特征选择来提高癌症基因识别的准确性和可解释性.
主要方法:
- 开发ICGI框架,将LLM与因果关系的上下文线索和数据驱动的因果特征选择相结合.
- 在癌症基因组图谱 (TCGA) 中,将因果特征选择模块应用于6种癌症类型的转录组数据.
- 将ICGI的性能与用于识别癌症区分基因的最先进方法进行比较.
主要成果:
- 与现有方法相比,ICGI框架在识别癌症基因方面表现出卓越的能力,能够有效地区分癌症和正常样本.
- 在发现癌症基因和理解疾病机制方面,LLM显示出潜力,特别是在基因组层面.
- 当前的LLM可能无法完全捕捉所有omics级别的信息,表明未来改进的领域.
结论:
- 拟议的ICGI框架提供了一个强大的和可解释的方法,用于多omics癌症基因识别.
- 该研究强调了将LLM与因果推理整合为推进癌症研究的潜力.
- 一个在线服务平台已经开发出来,提供基于因果学习方法的可访问的基因与癌症关联见解.
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