Integrating functional and taxonomic profiles for disease prediction

Music City Center 

Recently, the microbiome has gained significant attention as a potential predictor of human diseases. However, identifying robust, validated, and powerful microbial biomarkers remains challenging due to the complexity of microbiome data, including both taxonomic and functional profiles. Studies have shown that taxonomic profiles typically offer greater predictive performance and are easier to apply in practical and clinical settings but exhibit higher variability. In contrast, functional profiles are more stable and interpretable in terms of biological mechanisms but tend to have lower predictive performance. In this study, we propose a robust microbial risk score (MRS) framework that integrates both taxonomic and functional profiles to identify a microbial sub-community capable of serving as biomarkers for disease susceptibility. Specifically, we first identify a sub-community of microbial taxa associated with disease using the taxonomic profile, following a similar approach to our MRS version 1. We then expand this sub-community by incorporating additional microbial taxa based on their functional similarities with the identified taxa and calculate the weighted diversities of the sub-community as the proposed MRSs. Through comprehensive real-data analyses using human microbiome datasets from the curatedMetagenomicData R package, we demonstrate the utility of the proposed MRS framework for disease prediction. Moreover, the incorporation of functional profiles can be seamlessly integrated into other predictive methods, such as random forests, to enhance predictive performance.

Keywords: Microbial risk score; taxonomic profile; functional profile; disease prediction.