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

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 Result: Experimental results demonstrate that HFTC outperforms Mothur, RDP, Sintax, QIIME2, and CNN-Duong, achieving a Matthews correlation coefficient (MCC) of 95.31% despite uneven class distributions. Its overall accuracy (ACC) reaches 95.25%. At the species level, it attains a hierarchical accuracy (HA) of 95.10%, surpassing the best-performing deep learning baseline, CNN-Duong, by 3.2%. Moreover, HFTC exhibits the smallest discrepancy between ACC and HA (1.60%), in contrast to CNN-Duong, which shows the largest gap (35.00%), highlighting HFTC's superior hierarchical consistency. Discussion: HFTC offers a scalable and accurate approach for fungal taxonomic classification. Its compact feature representation and hierarchical architecture make it particularly suitable for microbial diversity research. The source code and datasets are publicly accessible at https://github.com/wjjw0731/HFTC/tree/master.