HFTC:使用低维嵌入特征的ITS序列的层次性真菌分类学分类模型
Jiawei Wang1, Shaojie Qiao1, Dongsheng Xiang1
1School of Software Engineering, Chengdu University of Information Technology, Chengdu, China.
Frontiers in genetics
|October 20, 2025
概括
一个新的真菌分类学分类器,HFTC,提高了生物多样性研究的准确性和效率. 这种层次的随机森林模型通过减少特征维度和捕捉序列上下文来增强真菌识别.
科学领域:
- 菌类学 菌类学是指菌类学.
- 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:
- 通过ITS测序识别真菌对生态和生物多样性研究至关重要.
- 现有的方法与高维数据和分类学不一致性作斗争.
研究的目的:
- 开发一种名为HFTC的新型层次性真菌分类学分类器.
- 提高真菌分类的准确性和计算效率.
主要方法:
- 为HFTC实施了一个多层随机森林 (RF) 架构.
- 使用双向k-mer策略和Word2Vec嵌入,将特征维度从4^k降低到200.
- 从两个方向捕获了上下文序列信息.
主要成果:
- HFTC实现了95.31%的马修斯相关系数 (MCC) 和95.25%的整体准确性 (ACC).
- 种级层次精度 (HA) 达到95.10%,超过深度学习基线CNN-Duong的3.2%.
- HFTC表现出卓越的等级一致性,ACC-HA差异最小 (1.60%).
结论:
- HFTC为真菌分类学分类提供了一个可扩展和准确的解决方案.
- 它的紧的特征表示和层次设计是微生物多样性研究的理想选择.
- 源代码和数据集是公开可用的.
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