AR-CDT NET: una red convolucional deformable profunda para la clasificación de enfermedades basada en el microbioma...

Advances in metagenomic sequencing have increasingly implicated gut microbiome dysbiosis in numerous complex diseases, yet its application for precise differential diagnosis remains a major challenge. Existing computational approaches often show limited predictive performance and insufficient robustness when applied to large-scale, imbalanced microbiome datasets, and they typically lack mechanisms to effectively capture microbial community-level or functional guild interactions. To address these limitations, we developed AR-CDT Net, a novel deep learning framework that integrates a Multi-Scale Deformable Convolution (MS-DConv) module with a Channel-wise Dynamic Tanh (CD-Tanh) activation function to achieve more accurate and robust classification of host disease states. Evaluated on a large-scale cohort comprising over 8000 samples spanning eight disease phenotypes, AR-CDT Net demonstrated highly competitive within-cohort performance, outperforming nine representative models across the majority of classification tasks. Importantly, in a stringent cross-dataset generalization test, the model was trained on the highly imbalanced primary multi-disease cohort and validated on relatively balanced independent external cohorts. It achieved a statistically significant AUC of 0.7921 on the highly heterogeneous external T2D cohort, confirming that AR-CDT captures transferable biological signals rather than dataset-specific artifacts. Furthermore, by combining dimensionality reduction with SHAP-based interpretation of our One-vs-Rest (OvR) classifiers, AR-CDT disentangles disease-specific pathogenic signatures from the shared dysbiotic background among clinically distinct yet microbially similar diseases.