Affiliation:
1. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, 410083, ChangSha, China
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 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.
Funder
Science and Technology Foundation of Guizhou Province of China
Hengyang Civic Science and Technology Foundation
Scientific Research Foundation of Hunan Provincial Education Department
Hunan Provincial Science and Technology Program
111 Project
National Natural Science Foundation of China
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
12 articles.
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