MHMDA: Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information

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MHMDA : Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information. / Wu, Chuanyan; Gao, Rui; Zhang, Yusen.

In: IEEE Access, Vol. 7, 8768373, 01.01.2019, p. 106687-106693.

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TY - JOUR

T1 - MHMDA

T2 - Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information

AU - Wu, Chuanyan

AU - Gao, Rui

AU - Zhang, Yusen

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Microbes are vital in human health. It is helpful to promote diagnostic and treatment of human disease and drug development by identifying microbe-disease associations. However, knowledge in this area still needs to be further improved. In this paper, a new computational model using matrix completion to predict human microbe-disease associations (mHMDA, Fig. 1) is developed. First, we extract the disease feature by Gaussian kernel-based similarity and symptom-based similarity. Meanwhile, the microbe feature is computed by Gaussian kernel-based similarity. As treating potential association as the missing elements of a matrix, the matrix completion is adopted to get the potential microbe-disease associations. Leave-one-out cross-validation (LOOCV) is carried out which get the AUC (The area under ROC curve) of 0.928 showing the effectiveness of mHMDA. Furthermore, 5-fold CV get the AUCs of 0.8838 ± 0.0044 (mean ± standard deviation). Moreover, through the four case studies (asthma, inflammatory bowel disease (IBD), type 2 diabetes (T2D), and type 1 diabetes (T1D)), we find that nine, ten, nine, and eight of top-ten inferred microorganisms for the four diseases are previously verified by experiments. All these results indicate the effectiveness of mHMDA. mHMDA might be helpful to infer the disease-related microorganisms.

AB - Microbes are vital in human health. It is helpful to promote diagnostic and treatment of human disease and drug development by identifying microbe-disease associations. However, knowledge in this area still needs to be further improved. In this paper, a new computational model using matrix completion to predict human microbe-disease associations (mHMDA, Fig. 1) is developed. First, we extract the disease feature by Gaussian kernel-based similarity and symptom-based similarity. Meanwhile, the microbe feature is computed by Gaussian kernel-based similarity. As treating potential association as the missing elements of a matrix, the matrix completion is adopted to get the potential microbe-disease associations. Leave-one-out cross-validation (LOOCV) is carried out which get the AUC (The area under ROC curve) of 0.928 showing the effectiveness of mHMDA. Furthermore, 5-fold CV get the AUCs of 0.8838 ± 0.0044 (mean ± standard deviation). Moreover, through the four case studies (asthma, inflammatory bowel disease (IBD), type 2 diabetes (T2D), and type 1 diabetes (T1D)), we find that nine, ten, nine, and eight of top-ten inferred microorganisms for the four diseases are previously verified by experiments. All these results indicate the effectiveness of mHMDA. mHMDA might be helpful to infer the disease-related microorganisms.

KW - matrix completion

KW - microbe-disease association prediction

KW - Microbial community

UR - http://www.scopus.com/inward/record.url?scp=85071156698&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2930453

DO - 10.1109/ACCESS.2019.2930453

M3 - Article

AN - SCOPUS:85071156698

VL - 7

SP - 106687

EP - 106693

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8768373

ER -