MHMDA : Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information
(2019) In IEEE Access 7. p.106687-106693- Abstract
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... (More)
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.
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- author
- Wu, Chuanyan LU ; Gao, Rui and Zhang, Yusen
- organization
- publishing date
- 2019-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- matrix completion, microbe-disease association prediction, Microbial community
- in
- IEEE Access
- volume
- 7
- article number
- 8768373
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85071156698
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2019.2930453
- language
- English
- LU publication?
- yes
- id
- d143904a-cda2-43e9-a7db-5a2860c7e0fa
- date added to LUP
- 2019-09-03 08:55:36
- date last changed
- 2024-04-16 18:36:22
@article{d143904a-cda2-43e9-a7db-5a2860c7e0fa, abstract = {{<p>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.</p>}}, author = {{Wu, Chuanyan and Gao, Rui and Zhang, Yusen}}, issn = {{2169-3536}}, keywords = {{matrix completion; microbe-disease association prediction; Microbial community}}, language = {{eng}}, month = {{01}}, pages = {{106687--106693}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Access}}, title = {{MHMDA : Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information}}, url = {{http://dx.doi.org/10.1109/ACCESS.2019.2930453}}, doi = {{10.1109/ACCESS.2019.2930453}}, volume = {{7}}, year = {{2019}}, }