Abstract
The 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 microbes will provide a basis for targeted treatment of the disease. In this paper, we propose an approach based on Kernelized Bayesian Matrix Factorization (KBMF) to predict microbe-disease association, based on the Gaussian interaction profile kernel similarity for microbes and diseases. The prediction performance of the method was evaluated by five-fold cross validation. KBMF achieved reliable results which is better than several state-of-the-art methods with around 8% improvement of AUC. Furthermore, case studies have demonstrated the reliability of the method.
S. Chen and D. Liu—Equal contribution
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Acknowledgement
This research is supported by the National Natural Science Foundation of China (No. 61532008), the Excellent Doctoral Breeding Project of CCNU, the Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (No. CCNU16KFY04).
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Chen, S., Liu, D., Zheng, J., Chen, P., Hu, X., Jiang, X. (2018). Predicting Microbe-Disease Association by Kernelized Bayesian Matrix Factorization. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_47
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