An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data

The gut microbiota plays a vital role in maintaining human health. In recent years, extensive researches has focused on phenotype prediction in relation to various diseases, with the gut microbiota as a key predictor. Nevertheless, most existing studies rely on single-time-point analyses, which are insufficient to capture the dynamic patterns of host states and temporal variations inherent in longitudinal data. In this study, we propose a deep learning framework, AWSD-CNN-LSTM, designed to classify host phenotypes using longitudinal metagenomic data. Unlike conventional approaches that treat each time point as an independent sample, our method models the sequential samples of each individual as a whole, integrating convolutional neural network (CNN) and long short-term memory network (LSTM) to effectively capture temporal dependencies in longitudinal microbiome sequencing data. In addition, the model incorporates an adaptive point-wise self-distillation mechanism to more accurately characterize host-specific patterns. Compared with state-of-the-art methods, AWSD-CNN-LSTM demonstrates superior performance on the PROTECT, DIABIMMUNE, and Infants datasets, achieving area under the receiver operating characteristic curve (AUC) values of 0.896, 0.813, and 0.894, respectively. For the task of disease phenotype classification based on temporal data, we propose a novel framework that effectively captures the characteristics of time-series data and achieves high accuracy across multiple datasets. Our approach holds promise as a potential new tool for microbial knowledge discovery.