HFTC: a hierarchical fungal taxonomic classification model for ITS sequences using low-dimensional embedding features.

ABSTRACT:

Introduction

Fungal identification through ITS sequencing is pivotal for biodiversity and ecological studies, yet existing methods often face challenges with high-dimensional features and inconsistent taxonomy predictions.

Method

We proposed HFTC, a hierarchical fungal taxonomic classifier built upon a multi-level random forest (RF) architecture. Notably, HFTC incorporates a bidirectional k-mer strategy to capture contextual information from both sequence orientations. By leveraging Word2Vec embedding, it reduces feature dimensionality from 4 ^k to only 200, significantly improving computational efficiency while preserving rich sequence context.

Result

Experimental results demonstrate that HFTC outperforms Mothur, RDP, Sintax, QIIME2, and CNN-Duong, a

SUBMITTER: Wang J 

PROVIDER: S-EPMC12531816 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature