  Many diseases are considered to be closely related to the changes in the gut
microbial community, including colorectal cancer (CRC), which is one of the
most common cancers in the world. The diagnostic classification and etiological
analysis of CRC are two critical issues worthy of attention. Many methods adopt
gut microbiota to solve it, but few of them simultaneously take into account
the complex interactions and individual heterogeneity of gut microbiota, which
are two common and important issues in genetics and intestinal microbiology,
especially in high-dimensional cases. In this paper, a novel method with a
Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with
the above problem. The binary matrix can directly weakened or avoided the
influence of heterogeneity, and also contain the information about gut
microbial interactions with any order. Moreover, LRBmat has a powerful
generalization, it can combine with any machine learning method and enhance
them. The real data analysis on CRC validates the proposed method, which has
the best classification performance compared with the state-of-the-art.
Furthermore, the association rules extracted from the binary matrix of the real
data align well with the biological properties and existing literatures, which
are helpful for the etiological analysis of CRC. The source codes for LRBmat
are available at https://github.com/tsnm1/LRBmat.
