## 1 Introduction
Additionally, tools like HUMAnN, StrainPhlAn, PanPhlAn, and PhyloPhlAn have been proven to be highly effective in various aspects of microbial community analysis, including strain-level resolution, phylogenetic insights, taxonomic classification, and functional profiling, as demonstrated in [22]. These innovative tools, exemplified by MetaPhlAn for comprehensive taxonomic profiling and by HUMAnN for precise functional analysis, significantly contribute to unraveling the intricate structure and functionality of microbial communities through sophisticated sequence-based and functional metagenomic approaches. The utilization of these cutting-edge tools not only enhances our understanding of the complex interactions within microbial ecosystems, but also paves the way for more in-depth research and insights into the role of microbiomes in various biological processes, including the development of diseases like colorectal cancer. Biomarker detection is crucial for early disease diagnosis, prognosis, and predicting treatment responses, facilitating medical interventions. Identifying actionable biomarkers can guide the selection of effective targeted therapies and avoid less effective treatments [23]. For biomarker detection, relying solely on statistical analysis can be insufficient [24]. To this end, the Grouping-Scoring-Modeling (G-S-M) approach offers to evaluate groups of features rather than assessing individual features. The G-S-M technique was recently proposed to integrate biological knowledge into machine learning models [25]. This method generates feature sets using either of the following: (i) pre-existing biological knowledge stored in databases such as mirTarBase [26], DisGeNET [27], or KEGG pathways [28]; or (ii) a data-driven approach relying on statistical measures, such as [PERSON]'s correlation. The current study seeks to integrate external biological knowledge into the colorectal-cancer-associated enzyme selection process in metagenomic data analysis, aiming to deliver results that are both statistically robust and biologically meaningful. Along this line, enzyme commission (EC) nomenclature was developed by the International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) in 1999. This system assigns each enzyme an EC number, consisting of the prefix \"EC\" followed by four digits separated by periods. For example, an EC number like EC 1.1.1.1 identifies a specific enzyme. The first three digits of the EC number represent the enzyme's class, subclass, and sub- subclass, respectively, providing insight into its general function. The seven main EC classes, including oxidoreductaseness (EC: 1), transferases (EC: 2), hydrolases (EC: 3), lyases (EC: 4), isomerases (EC: 5), ligases (EC: 6), and translocases (EC:7), categorize enzymes based on the type of chemical reactions that they facilitate (as depicted in Figure 1). To delve deeper into enzyme specificity, subclass and sub-subclass numbers further refine the classification, specifying the exact nature of the reaction and the substrates involved. For example, within the oxidoreductaseness class (EC: 1), there are 23 subclasses (e.g., EC 1.1-1.22 and EC 1.97), each representing a distinct group of enzymes with specific functions. By utilizing these detailed classification systems, researchers can gain a comprehensive understanding of the enzyme activities and their contributions to biochemical pathways, paving the way for advancements in fields such as metagenomics and disease-microbiome relationships. The research is centered on the identification of sets of enzymes linked by enzyme commission (EC) terms that are relevant to a specific disease. The main objective in this study is to help CRC diagnosis via classifying enzymes into their functional groups and then utilizing the Grouping-Scoring-Modeling (G-S-M) methodology to pinpoint highly correlated EC terms associated with the disease.