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托登-E:基于拓和基于密度的组合集群用于使用PAG网络和LLM在功能基因组学中开发超级PAG

Qi Li, Cody Nichols, Robert S Welner

bioRxiv : the preprint server for biology

|November 1, 2024

概括

我们开发了 todenE,这是一种新的集群方法,它结合了拓和密度来识别生物通路社区. 这种方法使用大型语言模型 (LLM) 来创建超级路径,改进omics数据解释.

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:

对OMIC数据的综合分析对生物医学研究至关重要.
路径,注释基因列表和基因签名 (PAG) 富含元数据以表示生物功能.
PAG-PAG网络是使用基因相似性和规则建立的,但冗余性阻碍了解释.

研究的目的:

开发一种新的聚类方法,用于检测生物通路社区.
通过分组类似的PAG来创建简洁的功能表示,称为超级PAG.
利用PAG网络和大型语言模型 (LLM) 进行增强的功能表征.

主要方法:

全天开发,集成基于拓和基于密度的集群.
使用PAG网络和LLM来捕获PAG-PAG相似性和功能信息.
引入差异指数 (DI) 来评估合成数据中的集群性.
员工转移学习和LLMs以基于语言的相似性嵌入非模拟数据.

主要成果:

TodenE有效地检测到PAG社区并形成超级PAG.
不平等指数 (DI) 有助于评估聚类性.
基于LLM的嵌入改善了超级PAG的语义和基因成员包容性.
在描述基于网络的功能性超级PAG方面,TodenE表现出卓越的性能.

结论:

TodenE提供了一种强大的方法来识别和组织生物途径.
整合LLM可以增强omics数据的功能解释.
由todenE生成的超级PAG提供了简洁而信息丰富的功能表示.

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