CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.

ABSTRACT: Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe-disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe-disease association matrix. Our CMFHMDA model is subsequent

SUBMITTER: Chen J 

PROVIDER: S-EPMC11427075 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature