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Updated: Jan 12, 2026

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aiGeneR 3.0:一种增强的深度网络模型,用于在Escherichia coli中识别耐药菌株和预测多药耐药性,该模型使用下一代测序数据导致尿路感染

Debasish Swapnesh Kumar Nayak^1,2,3, Abhilash Pati¹, Amrutanshu Panigrahi¹

¹Department of Computer Science & Engineering, Siksha "O" Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.

Frontiers in genetics

|November 7, 2025

概括

一个新的深度学习模型,aiGeneR 3.0,准确地识别了耐多药性大肠杆菌菌株. 这种工具有助于打击抗菌素耐药性 (AMR),并具有潜在的临床应用.

科学领域:

* 微生物学与传染病
* 生物信息学和计算生物学
* 医疗保健中的机器学习

背景情况:

*抗微生物耐药性 (AMR) 是一个重大的全球健康挑战,特别是对于常见的病原体,如大肠杆菌 (E. coli).
*大肠杆菌是尿路感染的常见原因,因此对其抗生素耐药性基因的研究对公共卫生至关重要.
*制定有效的策略来识别和打击耐药菌株是必不可少的.

研究的目的:

* 开发和评估aiGeneR 3.0,一种简化深度学习 (DL) 模型,用于识别多药耐药和耐药大肠杆菌菌株.
*与基线DL模型相比,评估模型的有效性,计算效率和概括能力.
*将质量控制与DL整合起来,以进行强大的抗生素耐药性识别.

主要方法:

* 实施一个深度学习模型,aiGeneR 3.0,利用长期短期记忆 (LSTM) 机制.
*利用下一代测序 (NGS) 数据进行菌株识别和分类.
*采用交叉验证来评估性能指标,包括ROC-AUC,F1得分,准确性,精度,灵敏度和特异性.

主要成果:

*aiGeneR 3.0实现了高分类准确性 (93%) 和多药耐药性预测准确性 (98%),即使使用不平衡或小数据集.
* 该模型展示了一个简单而有效的架构,适合处理各种数据.
*使用LSTM网络与NGS数据定位aiGeneR 3.0用于未来识别新型抗生素耐药性.

结论:

*aiGeneR 3.0为抗菌素耐药性分析提供了一种强大而普遍的方法.
*将单核酸多态 (SNP) 层面的洞察与DL集成,为抗生素管理提供了潜在的临床实用性.
* 该模型旨在为未来的适应性设计,用于识别新出现的阻力模式.

关键词:

抗生素耐药性基因是抗生素耐药性的基因.抗微生物耐药性抗微生物耐药性深度学习是一种深度学习.机器学习是机器学习.这是下一代测序.

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