Lei Wang1, Zhu-Hong You1, Yu-An Huang2, De-Shuang Huang3, Keith C C Chan2. 1. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China. 2. Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China. 3. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
Abstract
MOTIVATION: Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA-disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA-disease associations on a large scale and to provide the most promising candidates for biological experiments. RESULTS: In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA-disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA-disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA-disease associations and can provide reliable candidates for biological experiments. AVAILABILITY AND IMPLEMENTATION: The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA-disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA-disease associations on a large scale and to provide the most promising candidates for biological experiments. RESULTS: In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA-disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA-disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA-disease associations and can provide reliable candidates for biological experiments. AVAILABILITY AND IMPLEMENTATION: The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.