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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
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
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
2019-09-26 04:41:10
@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>},
  articleno    = {8768373},
  author       = {Wu, Chuanyan and Gao, Rui and Zhang, Yusen},
  issn         = {2169-3536},
  keyword      = {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},
  volume       = {7},
  year         = {2019},
}