NPENN: A Noise Perturbation Ensemble Neural Network for Microbiome Disease Phenotype Prediction

With advances in microbiomics, the crucial role of microbes in disease progression is increasingly recognized. However, predicting disease phenotypes using microbiome data remains challenging due to data complexity, heterogeneity, and limited model generalization. Current methods often depend on specific datasets and are vulnerable to adversarial attacks. To address these issues, this paper introduces a novel Noise Perturbation Ensemble Neural Network model (NPENN), which combines noise mechanisms with Gradient Boosting (GB) techniques for robust neural network ensemble learning. NPENN, validated on multiple microbiome datasets, shows superior accuracy and generalization compared to traditional methods, effectively handling data complexity and variability. This approach enhances model robustness and feature learning by integrating GB prior knowledge. Additionally, the study explores microbial community roles in various diseases, providing insights into disease mechanisms and potential biomarkers for personalized precision diagnosis and treatment strategies.