生物LM-NET:一种可解释的深度学习模型,结合了先前的生物学知识和在多omics数据上的上下文LLM基因嵌入,以预测疾病.
Jubair Ibn Malik Rifat1, Thasina Tabashum2, Md Marufi Rahman3
1Department of computer Science & Engineering, University of North Texas, Denton, Texas, 76203, USA, jubairibnmalikrifat@my.unt.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
生物LM-NET是一个新的深度学习框架,集成了基因表达,DNA甲基化和生物相互作用 (PPI,PDI) 来预测疾病的结果. 它的性能优于癌症和阿尔茨海默氏症的现有方法.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 深度神经网络 (DNN) 越来越多地用于生物研究,通常包含基因通路信息.
- 现有的DNN经常忽视关键的蛋白质-蛋白质相互作用 (PPI) 和蛋白质-DNA相互作用 (PDI).
研究的目的:
- 引入BioLM-NET,这是一个深度学习框架,将多omics数据与生物知识集成在一起.
- 通过结合PPI和PDI,提高疾病亚型和患者结果的预测准确度.
主要方法:
- 生物LM-NET将基因表达和DNA甲基化数据与PPI和PDI知识融合在一起.
- 一个基于注意力的路径层聚合了omics信号,利用预训练的大型语言模型 (LLM) 进行基因嵌入.
- 该框架被评估为单细胞结直肠癌,TCGA癌症亚型 (BRCA,GBM,COAD) 和阿尔茨海默病 (ROSMAP) 数据集.
主要成果:
- 在scTrioseq2,TCGA-COAD和ROSMAP数据集上,BioLM-NET显著超过了基线和最先进的方法 (P-NET,PASNet).
- 它在TCGA-BRCA数据上取得了与SVM和密集神经网络相似的结果.
- 废除研究证实了PPI,PDI和基于注意力的途径层的关键作用.
结论:
- 通过整合多omics数据和生物相互作用,BioLM-NET在疾病预测方面表现出卓越的表现.
- 该模型的关键特征得到了重要的基因本体学 (GO) 术语和KEGG通路的丰富.
- 这些特征有可能成为各种疾病的生物标志物或治疗点.
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