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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Journal of Translational Medicine 1/2017

Prediction of microbe–disease association from the integration of neighbor and graph with collaborative recommendation model

Zeitschrift:
Journal of Translational Medicine > Ausgabe 1/2017
Autoren:
Yu-An Huang, Zhu-Hong You, Xing Chen, Zhi-An Huang, Shanwen Zhang, Gui-Ying Yan
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12967-017-1304-7) contains supplementary material, which is available to authorized users.

Abstract

Background

Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe–disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale.

Methods

In this work, we developed a new computational model for predicting microbe–disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe–disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity.

Results

To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe–disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa.

Conclusions

Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe–disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://​github.​com/​yahuang1991/​NGRHMDA.
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