MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec

Abstract

Introduction: Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes.

Methods: In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions.

Results and discussion: Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

Keywords: Node2vec; XGBoost classifier; computational model; microbe–drug association prediction; singular value decomposition.

Figure 1

Flowchart of MDSVDNV.

Figure 2

Example of how to use SVD on the microbe–drug relationship matrix.

Figure 3

Illustration of the random walk procedure in node2vec.The walk just transitioned from t to v and is now evaluating its next step out of node v. Edge labels indicate search biases α.

Figure 4

ROC curves of five competitive methods on MDAD.

Figure 5

ROC curves of five competitive methods on aBiofilm.

Figure 6

PR curves of five competitive methods on MDAD.

Figure 7

PR curves of five competitive methods on aBiofilm.

Figure 8

Prediction performance achieved by MDSVDNV under a 5-fold CV.

Figure 9

Performance comparison between MDSVDNV-L, MDSVDNV-N, and MDSVDNV.