Prediction of ncRNA-Disease Association Based on Correntropy Induced Loss Matrix Factorization Model

In recent years, numerous studies have demonstrated a close connection between human diseases and the regulation of non-coding RNAs (ncRNAs). Predicting potential ncRNAs associated with disease can help provide critical information for diagnosis and treatment of disease, leading to better disease analysis and prevention. Building good algorithms for predicting associations between ncRNAs and disease is critical. Many current algorithms have poor performance in identifying the association between ncRNAs and diseases. As a method for predicting the association between ncRNAs and diseases, we develop a Matrix Factorization method based on the Correntropy Induced Loss (C-loss) function (C-lossMF). In our model, we first construct ncRNA similarity matrix and disease similarity matrix by considering some important similarity information, and extract effective information of ncRNA and disease from them. Next, we perform matrix decomposition of ncRNA-disease association matrix and apply $L2$ loss and C-loss. Then we add collaborative regularization of RNA similarity matrix and the collaborative regularization of disease similarity matrix to take full advantage of the information in the similarity matrix. In particular, we propose a method that combines semi-quadratic optimization and gradient descent to optimize the model. In the experiments, we utilize the five-fold cross validation method on four datasets to evaluate the performance of C-lossMF. Comparing this model with other advanced models, the results show that it performs better.