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Prediction of Microbe-drug Associations Based on Chemical Structures and the KATZ Measure

Author(s):

Lingzhi Zhu, Guihua Duan*, Cheng Yan and Jianxin Wang   Pages 1 - 13 ( 13 )

Abstract:


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 microbe-microbe network, the drug-drug network, and a known microbe-drug association network. Finally, we apply KATZ to identify potential association s 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 the 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.

Keywords:

Microbe-drug association, Chemical structures, Gaussian Interaction Profile, KATZ measure

Affiliation:

Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, 410083, ChangSha, Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, 410083, ChangSha, Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, 410083, ChangSha, Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, 410083, ChangSha



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