Predicting Microbe-Drug Association based on Similarity and Semi-Supervised Learning

Abstract

Increasing clinic evidences have showed that microbial communities play important roles in human health and disease. Predicting hidden microbe-drug associations can be helpful in understanding the microbe-drug association mechanisms in clinical treatment, drug discovery, combinations and repositioning. Some computational methods were proposed to predict the associations of microbes and drugs. However, the prediction performance of these methods needs to be improved. In this study, a new computational model (LRLSMDA) is proposed for identifying Microbe-Drug Associations based on the Laplacian Regularized Least Square algorithm. LRLSMDA integrates the chemical structure similarity of drugs and known microbe-drug associations. The microbe Gaussian Interaction Profile (GIP) kernel similarity is computed based on known microbe-drug associations. We compute the drug GIP kernel similarity and the drug chemical structure similarity based on known microbe-drug associations and drug chemical structures. The drug GIP kernel similarity and the drug chemical structure similarity are integrated into a more comprehensive drug similarity matrix by the linear weighted method. Finally, the Laplacian regularized least squares algorithm is applied to predict hidden microbe-drug associations. LRLSMDA has achieved the average Area Under the Curve (AUC) values of 0.8983±0.0019, 0.9043±0.0015 and 0.9095 in 5-fold Cross-Validation (5CV), 10-fold Cross-Validation (10CV) and Leave One Out Cross-Validation (LOOCV), respectively. These experimental results show that the prediction performance of LRLSMDA outperforms three compared models.