A neural network-based framework to understand the Type 2 Diabetes (T2D)-related alteration of the human gut microbiome

Abstract

To identify the microbial markers from the complex human gut microbiome for delineating the disease-related microbial alteration is of great interest. Here, we develop a framework combining neural network (NN) and random forest (RF), resulting in 40 marker species and 90 marker genes identified from the metagenomic dataset D1 (185 healthy and 183 type 2 diabetes (T2D) samples), respectively. Using these markers, the NN model obtains higher accuracy in classifying the T2D-related samples than machine learning-based approaches. The NN-based regression analysis determines the fasting blood glucose (FBG) is the most significant association factor (P<<0.05) in the T2D-related alteration of the gut microbiome. Twenty-four marker species that vary little across the case and control samples and are often neglected by the statistic-based methods greatly shift in different stages of the T2D development, implying that the cumulative effect of the markers rather than individuals likely drives the alteration of the gut microbiome.