Prediction of lncRNA-disease associations based on matrix factorization and neural network

More and more evidences have shown that lncRNAs are involved with various complex human diseases. The small minority of experimentally validated lncRNA-disease associations have created an urgent demand for computational prediction models. Although many related approaches have been proposed, there still have much room for improvement. To address the cold-start problem and accurately represent associations, this paper considers the prediction of lncRNA-disease associations as a recommendation problem and proposes a matrix factorization and neural network based method. Firstly, to better represent lncRNAs and diseases, their embeddings are learned based on matrix factorization. And then, features of associations are represented by integrating embeddings of lncRNAs and diseases. Finally, neural network is applied for predicting potential associations. Experimental results show that our method can achieve better performance than the state-of-the-art approaches from several perspectives.