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.
Highlights
As an important part of the human microbiome, microbes are mainly made up of bacteria, archaea, viruses and fungi etc
We introduce 5-fold cross-validation (5CV), 10-fold Cross-Validation (10CV) and Leave One Out Cross-Validation (LOOCV) to validate whether LRLSMDA is effective in identifying microbe-drug associations
The prediction performance of LRLSMDA is systematically evaluated by the cross validation framework
Summary
As an important part of the human microbiome, microbes are mainly made up of bacteria, archaea, viruses and fungi etc. Microbes are mainly made up of bacteria, archaea, viruses and fungi etc. Bacteria and viruses are to cause hundreds of human diseases (Geoghegan et al, 2016). Some researchers think that these diseases can result from the absence of beneficial functions or the introduction of maladaptive functions by invading microbes (Turnbaugh et al, 2007; Methé et al, 2012; Young, 2017). It is believed that restoring the absence of beneficial functions or eliminating harmful microbial activities is helpful to the treatment of certain diseases (Young, 2017; Huttenhower et al, 2012).
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