HKDE-LACM:通过k-mer和DNABERT-2嵌入融合与循环DE-BO优化进行乳酸细菌分类的混合模型
Jie Zou1, Weichi Liu2, Jinhui Dai1
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, Inner Mongolia, China.
BMC genomics
|September 26, 2025
概括
一个新的混合模型,HKDE-LACM,使用基因组数据准确地分类乳酸细菌 (LAB) 菌株. 这种先进的方法改进了现有的技术,用于更好的功能开发和针对性地利用LAB.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 乳酸细菌 (LAB) 在食品生产和临床环境中至关重要.
- 准确的LAB菌株分类对于其开发和应用至关重要.
- 当前的机器学习和深度学习方法在特征表示和基因组分类的概括性方面面临挑战.
研究的目的:
- 使用基因组序列开发乳酸细菌 (LAB) 菌株的先进分类模型.
- 提高基因组基础LAB分类的准确性和通用性.
- 为增强基因组分析整合多样化的特征表示.
主要方法:
- 开发了HKDE-LACM,一种混合分类模型,将高维k-mer频率特征与DNABERT-2上下文嵌入相结合.
- 实现了一个循环差异演化和贝叶斯优化与故障避免 (C-DBFA) 框架用于超参数优化.
- 对三个LAB数据集进行了10倍交叉验证,以评估模型性能.
主要成果:
- 与现有方法相比,HKDE-LACM模型表现出优越的性能.
- 在LAB菌株鉴定中实现了更高的分类准确性和稳定性.
- 通过对多个LAB基因组数据集进行严格的10倍交叉验证来验证.
结论:
- 通过整合语义嵌入来实现更丰富的基因组表征,HKDE-LACM克服了传统k-mer特征的局限性.
- 通过C-DBFA框架,可以自动优化特征提取器和分类器.
- 该模型显示出作为基于基因组的LAB分类和相关生物信息任务的工具的巨大潜力.
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