A novel deep learning method for predictive modeling of microbiome data. Issue 3 (14th May 2020)

• Record Type: Journal Article
• Authors: Wang, Ye; Bhattacharya, Tathagata; Jiang, Yuchao; Qin, Xiao; Wang, Yue; Liu, Yunlong; Saykin, Andrew J; Chen, Li
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-05-14
• Subjects: microbiome -- phylogeny -- deep learning -- machine learning -- prediction; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa073
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as drug response predictions and disease diagnosis. It is thus essential and desirable to build a prediction model for clinical outcomes based on microbiome data that usually consist of taxon abundance and a phylogenetic tree. Importantly, all microbial species are not uniformly distributed in the phylogenetic tree but tend to be clustered at different phylogenetic depths. Therefore, the phylogenetic tree represents a unique correlation structure of microbiome, which can be an important prior to improve the prediction performance. However, prediction methods that consider the phylogenetic tree in an efficient and rigorous way are under-developed. Here, we develop a novel deep learning prediction method MDeep (microbiome-based deep learning method) to predict both continuous and binary outcomes. Conceptually, MDeep designs convolutional layers to mimic taxonomic ranks with multiple convolutional filters on each convolutional layer to capture the phylogenetic correlation among microbial species in a local receptive field and maintain the correlation structure across different convolutional layers via feature mapping. Taken together, the convolutional layers with its built-in convolutional filters capture microbial signals at different taxonomic levels while encouraging local smoothing and preserving local connectivity induced by the phylogenetic tree. We use both simulation studies and real data applications to demonstrate that MDeep outperforms competing methods in both regression and binary classifications. Availability and Implementation: MDeep software is available at https://github.com/lichen-lab/MDeep Contact: [e-mail]