Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification

The human microbiome is crucial for health regulation and disease progression, presenting a valuable opportunity for health state classification. Traditional microbiome-based classification relies on pre-trained machine learning (ML) or deep learning (DL) models, which typically focus on microbial distribution patterns, neglecting the underlying relationships between microbes. As a result, model performance can be significantly affected by data sparsity, misclassified features, or incomplete microbial profiles. To overcome these challenges, we introduce Phylo-Spec, a phylogeny-driven deep learning algorithm that integrates multi-aspect microbial information for improved status recognition. Phylo-Spec fuses convolutional features of microbes within a phylogenetic hierarchy via a bottom-up iteration and significantly alleviates the challenges due to sparse data and inaccurate profiling. Additionally, the model dynamically assigns unclassified species to virtual nodes on the phylogenetic tree based on higher-level taxonomy, minimizing interferences from unclassified species. Phylo-Spec also captures the feature importance via an information gain-based mechanism through the phylogenetic structure propagation, enhancing the interpretability of classification decisions. Phylo-Spec demonstrated superior efficacy in microbiome status classification across two in silico synthetic data sets that simulate the aforementioned cases, outperforming existing ML and DL methods. Validation with real-world metagenomic and amplicon data further confirmed the model's performance in multiple status classification, establishing a powerful framework for microbiome-based health state identification and microbe-disease association. The source code is available at https://github.com/qdu-bioinfo/Phylo-Spec.IMPORTANCEThe human microbiome profoundly influences health and disease, but current computational tools often overlook the evolutionary relationships among microbes, leading to incomplete or inaccurate interpretations of complex microbial data. Phylo-Spec provides a new way to understand the microbiome by combining microbial abundance, taxonomy, and phylogeny within a unified deep learning framework. This model not only improves the accuracy of health status classification but also highlights key microbial contributors linked to disease. By capturing both microbial diversity and evolutionary context, Phylo-Spec bridges the gap between bioinformatics and biological insight, offering a powerful and interpretable approach for advancing microbiome-based diagnostics and precision medicine.