CACONET: a novel classification framework for microbial correlation networks

WSGMB: weight signed graph neural network for microbial biomarker identification

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.