Background: Microbial communities have important influences on our health and disease.
Identifying potential human microbe-drug associations will be greatly advantageous to explore
complex mechanisms of microbes in drug discovery, combinations and repositioning. Until
now, the complex mechanism of microbe-drug associations remains unknown.
Objective: Computational models play an important role in discovering hidden microbe-drug associations
because biological experiments are time-consuming and expensive. Based on chemical
structures of drugs and the KATZ measure a new computational model (HMDAKATZ) is proposed
for identifying potential Human Microbe-Drug Associations.
Methods: In HMDAKATZ, the similarity between microbes is computed using the Gaussian Interaction
Profile (GIP) kernel based on known human microbe-drug associations. The similarity
between drugs is computed based on known human microbe-drug associations and chemical structures.
Then, a microbe-drug heterogeneous network is constructed by integrating the microbemicrobe
network, the drug-drug network, and a known microbe-drug association network. Finally,
we apply KATZ to identify potential associations between microbes and drugs.
Results: The experimental results showed that HMDAKATZ achieved area under the curve
(AUC) values of 0.9010±0.0020, 0.9066±0.0015, and 0.9116 in 5-fold cross-validation (5-fold
CV), 10-fold cross-validation (10-fold CV), and leave one out cross-validation (LOOCV), respectively,
which outperformed four other computational models(SNMF,RLS,HGBI, and NBI).
Conclusion: HMDAKATZ obtained better prediction performance than four other methods in 5-
fold CV, 10-fold CV, and LOOCV. Furthermore, three case studies also illustrated that HMDAKATZ
is an effective way to discover hidden microbe-drug associations.