A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction

Discussion There are numerous microbial communities inhabited in the human body, which is critical to human health. The relationship between human microbiome and diseases received much attention from both medical and bioinformatics community recently. However, traditional methods to detect their association is costly and labor-intensive. Thus, we proposed here a new computational model called NBLPIHMDA to infer potential microbe-disease associations. NBLPIHMDA first combined known microbe-disease associations in HMDAD and the Gaussian interaction profile kernel similarity to construct disease similarity network and microbe similarity network. It then conducted tag transmission on these two networks to obtain the predicted score of each microbe-disease pair. Under the framework of LOOCV and 5-fold CV, the AUCs reached 0.8777 and 0.8958 ± 0.0027, respectively, In addition, the case studies of asthma, colorectal carcinoma and COPD further demonstrated that NBLPIHMDA could provide valuable insights into the pathogenesis research. It worth's noting that NBLPIHMDA has certain limitations. First of all, the HMDAD only curated hundreds of known associations between 39 diseases and 292 microbes, which is relatively small. The problem will be partially solved in the future when more associations between diseases and microbes are discovered. In addition, the Gaussian interaction profile kernel similarity of diseases and microbes is calculated based on the known microbe...
Source: Frontiers in Microbiology - Category: Microbiology Source Type: research