Predicting Microbe-Metabolite Interactions by Integrating Non-negative Matrix Factorization and Generative Network.

Despite profound impacts on human health and nature, accurately predicting microbe-metabolite interactions remains challenging due to inherent data noise. This study applies non-negative matrix factorization (NMF) and multi-view NMF to reduce noise and exploit associations across data perspectives. NMF obtains low-dimensional microbial and metabolic representations, effectively reducing noise. The dimension-reduced spectral matrices were input into the generative network model to derive conditional probabilities of individual microbe-associated metabolites and microbe-metabolite co-occurrence probabilities, the latter enabling prediction of microbe-metabolite interactions. Moreover, multi-view NMF integrates microbial and metabolic data by mapping them into a shared subspace, thereby enhancing prediction performance and validating cross-perspective correlation modeling. This study demonstrates NMF's efficacy in noise reduction through dimensionality reduction, and multiview NMF's ability to leverage cross-view associations. Both approaches demonstrate enhanced microbe-metabolite interaction prediction utilizing NMF-based and multi-view NMF-based generative network models.