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

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

Volume: 16 Issue: 6

Author(s): Lingzhi Zhu, Guihua Duan*, Cheng Yan and Jianxin Wang

Affiliation:

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

Keywords: Microbe-drug association, chemical structures, gaussian interaction profile, KATZ measure, microbe, drug.

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 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.

Cite this article as:

Zhu Lingzhi , Duan Guihua *, Yan Cheng and Wang Jianxin , Prediction of Microbe-drug Associations Based on Chemical Structures and the KATZ Measure, Current Bioinformatics 2021; 16 (6) . https://dx.doi.org/10.2174/1574893616666210204144721