In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a minimizing Matrix Nuclear Norm method into known microbe and disease data. Specifically, we explored literatures and related databases to obtain high-quality known relationship between microbes and diseases. By incorporating multiple similarity networks between microbes and diseases, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms.