GSPHI:一种新的深度学习模型,通过多种生物信息来预测菌体与宿主相互作用.
Jie Pan1, Wencai You1, Xiaoliang Lu1
1Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi'an 710069, China.
Computational and structural biotechnology journal
|July 3, 2023
概括
菌体疗法为抗生素耐药细菌提供了一个有希望的解决方案. 这项研究介绍了GSPHI,一种深度学习模型,可以使用序列数据准确预测菌体与宿主相互作用,有助于开发新的抗菌策略.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 抗生素耐药性是一个日益增长的全球健康威胁.
- 菌体 (菌体) 疗法是治疗细菌感染的有希望的替代方案.
- 鉴定菌体与宿主相互作用 (PHI) 对于理解细菌反应和开发有效的菌体疗法至关重要.
研究的目的:
- 开发一个用于预测菌体与宿主相互作用 (PHI) 的计算框架.
- 利用DNA和蛋白质序列信息进行准确的PHI预测.
- 为PHI识别提供一种高效和经济的替代传统湿实验室实验.
主要方法:
- 开发了GSPHI,这是一个使用自然语言处理进行节点表示初始化的深度学习框架.
- 员工结构深度网络嵌入 (SDNE) 来提取网络信息.
- 应用了深度神经网络 (DNN) 来预测菌-细菌相互作用.
- 在ESKAPE数据集上使用5倍交叉验证验证模型.
主要成果:
- 在ESKAPE数据集上,GSPHI实现了86.65%的预测准确率和0.9208的AUC.
- 该模型在预测PHI方面明显优于现有方法.
- 案例研究证明了GSPHI在识别格兰正和格兰阴性细菌之间的相互作用方面的能力.
结论:
- GSPHI是一种有效的深度学习工具,用于预测菌体与宿主相互作用.
- 该框架为实验验证提供了有价值的菌体敏感细菌候选人.
- GSPHI提供了一种节省时间和成本的方法,以推进菌体疗法研究.
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