Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low
Bayesian algorithmConjugate probabilistic modelMatrix factorizationMany biological experimental studies have confirmed that microRNAs (miRNAs) play a significant role in human complex diseases. Exploring miRNA-disease associations could be conducive to understanding disease pathogenesis at the molecular level and ...
In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miRNAs and diseases with several information such as miRNA functional similarity, disease semantic similarity, and known relations among miRNAs and diseases. AUC value of 0.9450 was ...
Compared with Bayesian probabilistic matrix factorization, which integrates a Gaussian prior for single row of the data matrix, our proposed model, namely Bayesian hierarchical kernelized probabilistic matrix factorization, imposes Gaussian Process priors over multiple rows of the matrix. Hence, the learned...
Probabilistic matrix factorizationWe present a Bayesian analysis framework for matridoi:10.1080/03610918.2014.906612WangIBMJunIBMYangIBMHongxiaIBMCommunications in statistics, B. Simulation and computationBayesian hierarchical kernelized probabilistic matrix factorization. Yang Hong-xia,Wang Jun. Communication in ...
MicrobeMatrix factorizationBayesianBiological networkThe study of microbe-disease associations can be utilized as a valuable material for understanding disease pathogenesis. Developing a highly accurate algorithm model for predicting disease-related...