通过特征空间平衡来改善分类学分类
Wolfgang Fuhl1, Susanne Zabel1, Kay Nieselt1
1University of Tübingen, Institute for Biomedical Informatics (IBMI), Sand 14, Tübingen, Baden-Württemberg, 72076, Germany.
Bioinformatics advances
|August 14, 2023
概括
我们引入了使用k-mer配置文件和特征平衡进行分类学分类的新方法,大大提高了元基因组测序的准确性. 这种方法优于现有的算法,甚至可以对新生物进行分类.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 微生物生态学 微生物生态学
背景情况:
- 高通量测序产生数百万个需要分类分配的DNA序列.
- 目前的MMseqs2 (基于调整) 和BERTax (深度学习) 等方法面临着大数据集,运行时间,硬件和能源消耗等挑战.
研究的目的:
- 开发一种更有效,更准确的对元基因组数据的分类法.
- 通过特征空间平衡来增强k-mer配置文件的预测能力.
主要方法:
- 使用DNA序列的k-mer配置文件作为分类学分类的特征.
- 应用了一种新的特征空间平衡方法来训练数据,以改善分类器概括.
- 实现的管道结合特征提取和平衡与分类器,如袋装决策树和特征子空间 KNNs.
主要成果:
- 拟议的管道显著提高了k-mer配置文件的预测能力.
- 在两个数据集上,与最先进的算法 (BERTax,MMseqs2) 相比,实现了更高的性能.
- 经过训练数据中不存在的生物体的序列分类的高精度.
结论:
- 具有特征平衡的k-mer配置方法为分类学分类提供了一个高度有效和高效的解决方案.
- 这种方法显示了在元基因组数据集中准确分类已知和新型微生物物种的前景.
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