一个强大的蛋白质语言模型用于SARS-CoV-2蛋白质-蛋白质相互作用网络预测
1Department of Computer Engineering, Sutcu Imam University, 46040, Kahramanmaras, Turkey.
Artificial intelligence in medicine
|June 14, 2023
概括
这项研究引入了一种新的自然语言处理方法,用于预测SARS-CoV-2-人类蛋白质-蛋白质相互作用 (PPI). 该方法有效地识别了潜在的相互作用,有助于理解病毒感染机制.
科学领域:
- 计算生物学 计算生物学
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
背景情况:
- 病毒感染,如SARS-CoV-2大流行,涉及病毒和宿主之间复杂的蛋白质-蛋白质相互作用 (PPI).
- 了解这些SARS-CoV-2-人体PPI对于阐明病毒复制,病原和疾病监测至关重要.
研究的目的:
- 提出和评估一种基于自然语言处理 (NLP) 的集体学习方法,用于预测SARS-CoV-2-人类PPI.
- 评估蛋白质语言模型与PPI预测中的传统特征提取方法相比的有效性.
主要方法:
- 使用word2Vec,doc2Vec和术语频率逆文档频率 (tf-idf) 生成的蛋白质语言模型.
- 训练有素的机器学习模型 (SVM,ANN,KNN,Naive Bayes,DT,ensemble) 在已知的交互上,由语言模型和传统特征 (联合三元组,重复模式) 代表.
- 采用集体投票方法,将高性能模型的决策结合起来,用于新型交互预测.
主要成果:
- 蛋白质语言模型对蛋白质-蛋白质相互作用预测有显著的前景.
- 基于tf-idf的语言模型在SARS-CoV-2 PPI估计中实现了1.4%的低误差率.
- 集体投票策略成功预测了10,000个人类蛋白质中285个新的潜在的SARS-CoV-2人类相互作用.
结论:
- 基于NLP的蛋白质语言模型为预测病毒宿主PPI提供了强大而准确的方法.
- 集体学习策略提高了预测准确度,并确定了与SARS-CoV-2感染相关的新型相互作用.
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