Abstract
The research on microorganisms indicates that microbes are abundant in human body, which have closely connection with various human noninfectious diseases. The deep research of microbe-disease associations is not only helpful to timely diagnosis and treatment of human diseases, but also facilitates the development of new drugs. However, the current knowledge in this domain is still limited and far from complete. Here, we proposed the computational model of Collaborative Matrix Factorization for Human Microbe-Disease Association prediction (CMFHMDA) by integrating known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. A special matrix factorization algorithm was introduced here to update the correlation matrix about microbes and diseases for inferring the most possible disease-related microbes. Leave-one-out Cross Validation (LOOCV) and k-fold cross Validation were implemented to evaluate the prediction performance of this model. As a result, CMFHMDA obtained AUCs of 0.8858 and 0.8529 based on 5-fold cross validation and Global LOOCV, respectively. It is no doubt that CMFHMDA could be used to identify more potential microbes associated with important noninfectious human diseases.
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Acknowledgement
This work was supported by the grants of the National Science Foundation of China, Nos. 61520106006, 31571364, U1611265, 61672203, 61402334, 61472280, 61472282, 61532008, 61472173, 61572447, 61373098 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646.
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Shen, Z., Jiang, Z., Bao, W. (2017). CMFHMDA: Collaborative Matrix Factorization for Human Microbe-Disease Association Prediction. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_24
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