Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.06.284885v1?rss=1 Authors: Guo, S., Zhang, H., Chu, Y., Jiang, q., Ma, Y. 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. Copy rights belong to original authors. Visit the link for more info