Abstract
High-throughput sequencing generates massive microbiome data, aiding the study of microbe-disease relationships. However, current analytical frameworks fail to precisely leverage taxonomic information, leading to suboptimal accuracy and interpretability due to community complexity and limited sample sizes. To explicitly integrate taxonomic information into a deep learning architecture, developing a structurally interpretable framework that achieves accurate prediction of clinically relevant features. We propose DeepNTax, a deep neural network model regularized using taxonomic information. Alongside abundance data, the model incorporates two key regularization components: the taxonomic divergence degree between connected taxa and the taxonomic rank level. In two real data applications, DeepNTax consistently provides better or competitive predictive performance compared to the comparison methods. In the hepatocellular carcinoma dataset, DeepNTax demonstrates competitive predictive performance with superior stability in Test AUC compared to baseline models. In the colorectal cancer dataset, the model achieves performance comparable to existing baselines, with no statistically significant differences in predictive power. By embedding taxonomic regularization for hierarchical representation learning, DeepNTax offers a robust predictive framework that maintains better or competitive performance while providing a structured mechanism for exploring microbial associations. This hierarchical approach facilitates the identification of taxonomically grounded patterns, offering a promising tool for interpretability in complex microbiome studies.
Subjects
- Computational biology and bioinformatics
- Ecology
- Microbiology
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2022R1A2C1092497), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (IRIS RS-2025-00560397).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1. (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Heo, G., Liu, Z., Chung, Y. et al. A taxonomically informed deep neural network with hierarchical regularization for microbiome data analysis.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-65015-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-65015-4
Keywords
- Deep learning
- Microbiome
- Disease prediction
- Taxonomic regularization
- Interpretability
Source: Ecology - nature.com
