深度学习神经网络开发用于乳酸细菌产生的细菌序的分类
Lady L González1, Isaac Arias-Serrano1, Fernando Villalba-Meneses1
1School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, Provincia de Imbabura, 100119, Ecuador.
F1000Research
|August 11, 2025
概括
一个新的深度学习模型准确地将细菌素从乳酸细菌 (LAB) 进行分类,有助于抗微生物发现. 该工具识别了设计新型抗生素对抗耐药细菌的保存动机.
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:
- 抗生素耐药性需要新的抗菌剂.
- 细菌素,特别是来自乳酸细菌 (LAB) 的细菌素,是有希望的天然替代品.
- 实验室细菌素被认定为普遍公认的安全 (GRAS) 和合格的安全假设 (QPS).
研究的目的:
- 开发一种深度学习模型,根据其LAB来源对细菌素进行分类.
- 为了利用可解释的k-mer特征和嵌入载体用于抗微生物发现应用.
- 为了能够识别具有治疗潜力的新型细菌素.
主要方法:
- 一个深度学习的神经网络被开发用于细菌氨基酸序列的二元分类.
- 使用k-mers (k=3,5,7,15,20) 和矢量嵌入 (EV) 来提取特征.
- 该模型在Google Colab上接受了训练,证明了计算可访问性.
主要成果:
- 5-mers,7-mers和EV特征的组合实现了最高的性能 (90.14%的准确性).
- 交叉验证显示了具有低损失和高精度,回忆和F1分数的强大性能.
- 已经确定了LAB细菌素特有的保存k-mer基因.
结论:
- 开发的深度学习模型显示了高准确性,并超过了现有方法.
- 在资源有限的环境中,该模型的可行性通过云平台得到证实.
- 鉴定的k-mers可以指导合成抗微生物药物的设计,等待实验验证.
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