Metabolite-disease association prediction algorithm combining DeepWalk and random forest

Abstract

Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases, which has great significance in diagnosing and treating diseases. However, traditional biometric methods are time consuming and expensive. Accordingly, we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest (DWRF), which consists of the following key steps: First, the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity. Similarly, molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity. Then, DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations. Finally, a random forest algorithm is employed to infer metabolite-disease associations. The experimental results show that DWRF has good performances in terms of the area under the curve value, leave-one-out cross-validation, and five-fold cross-validation. Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.