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

• Published in: Journal of theoretical biology Vol. 571; p. 111538
• Main Authors: Tang, Shan, Mao, Shanjun, Chen, Yangyang, Tan, Falong, Duan, Lihua, Pian, Cong, Zeng, Xiangxiang
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 21.08.2023
• Subjects: Association rule mining; Biomedical classification; Colorectal cancer; Colorectal Neoplasms; Gastrointestinal Microbiome; Gut microbial interactions; Gut microbiota; Humans; Individual heterogeneity; Microbial Interactions; Microbiota; Gut microbiota; Biomedical classification; Association rule mining; Individual heterogeneity; Gut microbial interactions; Colorectal cancer
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2023.111538

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. •LRBmat can more effectively address individual heterogeneity and interactions among gut microbes simultaneously.•LRBmat can be readily extended to other common classification models, with potential for significant improvements.