Background: Circular RNA (circRNA) has been extensively identified in cells and\ntissues, and plays crucial roles in human diseases and biological processes. circRNA\ncould act as dynamic scaffolding molecules that modulate protein-protein interactions.\nThe interactions between circRNA and RNA Binding Proteins (RBPs) are also deemed to\nan essential element underlying the functions of circRNA. Considering cost-heavy and\nlabor-intensive aspects of these biological experimental technologies, instead, the highthroughput\nexperimental data has enabled the large-scale prediction and analysis of\ncircRNA-RBP interactions.\nResults: A computational framework is constructed by employing Positive Unlabeled\nlearning (P-U learning) to predict unknown circRNA-RBP interaction pairs with kernel\nmodel MFNN (Matrix Factorization with Neural Networks). The neural network is\nemployed to extract the latent factors of circRNA and RBP in the interaction matrix, the\nP-U learning strategy is applied to alleviate the imbalanced characteristics of data\nsamples and predict unknown interaction pairs. For this purpose, the known circRNARBP\ninteraction data samples are collected from the circRNAs in cancer cell lines\ndatabase (CircRic), and the circRNA-RBP interaction matrix is constructed as the input of\nthe model. The experimental results show that kernel MFNN outperforms the other\ndeep kernel models. Interestingly, it is found that the deeper of hidden layers in neural\nnetwork framework does not mean the better in our model. Finally, the unlabeled\ninteractions are scored using P-U learning with MFNN kernel, and the predicted\ninteraction pairs are matched to the known interactions database. The results indicate\nthat our method is an effective model to analyze the circRNA-RBP interactions.\nConclusion: For a poorly studied circRNA-RBP interactions, we design a prediction\nframework only based on interaction matrix by employing matrix factorization and\nneural network. We demonstrate that MFNN achieves higher prediction accuracy, and it\nis an effective method.
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