Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA–disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.Author summary: Many studies have shown that miRNAs contribute to the onset and development of disease through a variety of potential mechanisms. Therefore, predicting multi-category associations between miRNAs and diseases is helpful for the diagnosis and treatment of complex diseases. Different from existing methods, our KBLTDARD combines the logistic tensor decomposition and Bayesian methods, realises the automatic search of hyperparameters by introducing sparse induction priors of multiple latent variables, and incorporates auxiliary information to improve the predictive ability of the Bayesian model. Unlike the simple overfitting of point estimation and the slow convergence of MCMC, we developed an efficient deterministic Bayesian inference algorithm to ensure solution efficiency. We performed 5-fold cross-validation to evaluate the performance of KBLTDARD, which can outperform other state-of-the-art methods. Moreover, the average AUC value of KBLTDARD for the prediction of 447 diseases reached 0.8165. For the prediction of miRNAs and association types of four common diseases, 17, 16, 16 and 17 of the top 20 triples were verified from the literature.