期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 18, 期 5, 页码 1763-1772出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3018138
关键词
Diseases; Computational modeling; Predictive models; Neural networks; Databases; Computer science; Encoding; Microbe-disease associations; neural network; pairwise loss; Bayesian personalized ranking
类别
资金
- National Natural Science Foundation of China [61672011, 61474267]
- National Key R&D Program of China [2017YFC1311003]
The study introduces a novel deep learning model DMFMDA for predicting microbe-disease associations, leveraging a combination of different modeling techniques. Experimental results demonstrate its excellent performance in disease prediction and validation in case studies of diseases like asthma, inflammatory bowel disease, and colon cancer.
Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.
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