ABSTRACTAdvances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological signals that pave the way for better and accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. There have been few attempts to uncover microbial community associations with certain health conditions. However, such attempts have mostly used partial or incomplete data to accurately capture those associations. This study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data. When comparing IBD to a control group, we show that co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment. The obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that classify or diagnose certain health conditions.CCS CONCEPTS• Applied computing→Biological networks;Bioinformatics;Systems biology.ACM Reference FormatSuyeon Kim, Ishwor Thapa, and Hesham Ali. 2023. A Novel Computational Approach for the Mining of Signature Pathways Using Species Co-occurrence Networks in Gut Microbiomes. InProceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 10 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn