Human host status inference from temporal microbiome changes via recurrent neural networks
| Autor: | Ka-Chun Wong, Jianyi Yang, Lingjing Liu, Xingjian Chen, Weitong Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data Analysis
Computer science Feature extraction Inference Machine learning computer.software_genre Data preparation 03 medical and health sciences 0302 clinical medicine Deep Learning RNA Ribosomal 16S Humans Microbiome Molecular Biology 030304 developmental biology 0303 health sciences Host Microbial Interactions business.industry Deep learning Microbiota Computational Biology Recurrent neural network Metagenomics Artificial intelligence Neural Networks Computer business computer Host (network) 030217 neurology & neurosurgery Algorithms Information Systems |
| Zdroj: | Briefings in bioinformatics. 22(6) |
| ISSN: | 1477-4054 |
| Popis: | With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the host status is predicted from longitudinal microbiome data. However, single-point-based methods cannot capture the dynamic patterns between the temporal changes and host status. Therefore, it remains challenging to build good predictive models as well as scaling to different microbiome contexts. On the other hand, existing methods are mainly targeted for disease prediction and seldom investigate other host statuses. To fill the gap, we propose a comprehensive deep learning-based framework that utilizes longitudinal microbiome data as input to infer the human host status. Specifically, the framework is composed of specific data preparation strategies and a recurrent neural network tailored for longitudinal microbiome data. In experiments, we evaluated the proposed method on both semi-synthetic and real datasets based on different sequencing technologies and metagenomic contexts. The results indicate that our method achieves robust performance compared to other baseline and state-of-the-art classifiers and provides a significant reduction in prediction time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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