Human disease prediction from microbiome data by multiple feature fusion and deep learning

Xingjian Chen; Zifan Zhu; Weitong Zhang; Yuchen Wang; Fuzhou Wang; Jianyi Yang; Ka-Chun Wong

doi:10.1016/j.isci.2022.104081

Human disease prediction from microbiome data by multiple feature fusion and deep learning

iScience. 2022 Mar 16;25(4):104081. doi: 10.1016/j.isci.2022.104081. eCollection 2022 Apr 15.

Authors

Xingjian Chen¹, Zifan Zhu², Weitong Zhang¹, Yuchen Wang¹, Fuzhou Wang¹, Jianyi Yang³, Ka-Chun Wong^{1

4}

Affiliations

¹ Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
² Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
³ School of Mathematical Sciences, Nankai University, Tianjin, China.
⁴ Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.

Abstract

Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.

Keywords: Biological sciences; Physiology; Systems biology.