A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations

• Main Authors: Shiru Li, Minzhu Xie, Xinqiu Liu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-11-01
• Series: Frontiers in Genetics
• Subjects: microbe; disease; KATZ model; bipartite network recommendation; Gaussian interaction profile kernel similarity
• Online Access: https://www.frontiersin.org/article/10.3389/fgene.2019.01147/full

Accumulating evidence indicates that the microbes colonizing human bodies have crucial effects on human health and the discovery of disease-related microbes will promote the discovery of biomarkers and drugs for the prevention, diagnosis, treatment, and prognosis of diseases. However clinical experiments of disease-microbe associations are time-consuming, laborious and expensive, and there are few methods for predicting potential microbe-disease association. Therefore, developing effective computational models utilizing the accumulated public data of clinically validated microbe-disease associations to identify novel disease-microbe associations is of practical importance. We propose a novel method based on the KATZ model and Bipartite Network Recommendation Algorithm (KATZBNRA) to discover potential associations between microbes and diseases. We calculate the Gaussian interaction profile kernel similarity of diseases and microbes based on validated disease-microbe associations. Then, we construct a bipartite graph and execute a bipartite network recommendation algorithm. Finally, we integrate the disease similarity, microbe similarity and bipartite network recommendation score to obtain the final score, which is used to infer whether there are some novel disease-microbe interactions. To evaluate the predictive power of KATZBNRA, we tested it with the walk length 2 using global leave-one-out cross validation (LOOV), two-fold and five-fold cross validations, with AUCs of 0.9098, 0.8463 and 0.8969, respectively. The test results also show that KATZBNRA is more accurate than two recent similar methods KATZHMDA and BNPMDA.