AI-based Discovery of Gut Microbiome Biomarkers in Type 2 Diabetes

Abstract

The gut microbiome plays an important role in maintaining human health, and changes in its composition have been closely linked to metabolic disorders such as Type 2 Diabetes. With the availability of high-throughput sequencing technologies, large amounts of gut microbiome data can now be generated. However, analysing these complex datasets using traditional methods can be difficult and time-consuming. Artificial intelligence (AI) provides an effective approach to handle such large and complex biological data and to identify meaningful disease-related patterns. This study focuses on the AI-based discovery of gut microbiome biomarkers associated with Type 2 Diabetes using publicly available metagenomic datasets. Bioinformatics techniques are first used to process raw sequencing data, including quality control, taxonomic classification, and feature extraction. The processed data are then analysed using AI and machine learning models to identify specific microbial features that distinguish individuals with Type 2 Diabetes from healthy controls. Unlike conventional statistical methods, AI-based approaches can efficiently manage high-dimensional data and capture complex relationships between microbial communities and disease states. By applying AI-driven analysis, this study aims to improve the accuracy and reliability of microbiome biomarker identification. The discovered microbial biomarkers may help in understanding the role of gut microbes in the development of Type 2 Diabetes. Overall, this work highlights the potential of integrating bioinformatics and artificial intelligence as a powerful computational framework for microbiome-based biomarker discovery, which may support early diagnosis and contribute to future microbiome-focused therapeutic strategies for Type 2 Diabetes.