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

Wu, Chuanyan LU ; Gao, Rui and Zhang, Yusen (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
; and
organization
publishing date
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}},
}