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Biomed Res Int. 2018 Jan 11;2018:2936257. doi: 10.1155/2018/2936257. eCollection 2018.

Metagenomics Biomarkers Selected for Prediction of Three Different Diseases in Chinese Population.

Wu H1,2,3, Cai L1,4, Li D1,2,3, Wang X2,3, Zhao S5, Zou F6, Zhou K1.

Author information

1
Wuhan National Laboratory for Optoelectronics, Key Laboratory of Information Storage System, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China.
2
Binhai Genomics Institute, BGI-Tianjin, BGI-Shenzhen, Tianjin 300308, China.
3
Tianjin Translational Genomics Center, BGI-Tianjin, BGI-Shenzhen, Tianjin 300308, China.
4
School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, Guangdong 524088, China.
5
BGI-Shenzhen, Shenzhen 518083, China.
6
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China.

Abstract

The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an F1 score of 0.9142, which was better than other popular classification methods, and an average receiver operating characteristic (ROC) of 0.9475 showed a significant correlation between these selected biomarkers from microbiome and corresponding phenotypes. The results from this study indicate that machine learning is a vital tool in data mining from microbiome in order to identify disease-related biomarkers, which may contribute to the application of microbiome-based precision medicine in the future.

PMID:
29568746
PMCID:
PMC5820663
DOI:
10.1155/2018/2936257
[Indexed for MEDLINE]
Free PMC Article

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