LLM4THP:一种计算工具,通过基于双层合奏模型战略的大型语言模型的分子和序列表示来识别瘤指导
1School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China.
Amino acids
|October 15, 2024
概括
LLM4THP是一种使用大型语言模型 (LLM) 快速检测瘤指导 (THP) 的新计算方法. 这种方法显著改进了传统方法,用于识别针对性癌症治疗至关重要的.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 癌症研究 癌症研究
背景情况:
- 瘤指导 (THPs) 对于向癌症治疗和检测至关重要.
- 传统的THP检测方法耗时且昂贵.
研究的目的:
- 开发一种高效的计算方法,LLM4THP,用于快速而准确的THP检测.
- 利用大型语言模型 (LLM) 和序列特征来增强的识别.
主要方法:
- 使用两个蛋白质LLM (ESM2,Prot_T5_XL_UniRef50) 来进行序编码.
- 嵌入了序列特征,如氨基酸组成 (AAC),伪氨基酸组成 (PAAC),两性伪氨基酸组成 (APAAC) 和组成,过渡和分布 (CTD).
- 使用RDKitDescriptors进行化学特征计算和两层组合模型 (LightGBM,XGBoost,随机森林,极端随机树和物流回归).
主要成果:
- 与现有的最先进的方法相比,LLM4THP表现出更高的性能.
- 在准确性,马修的相关系数,F1得分,AUC和平均精度方面观察到显著的改进.
- 该方法有效地识别瘤指导.
结论:
- LLM4THP为THP检测提供了一个快速有效的计算解决方案.
- 整合LLM和多种序列特征可以提高识别准确度.
- 这种方法对推进向癌症治疗和诊断有前途.
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