Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Genome Medicine

Zhixue Chen¹  Xuefeng Cui¹  Hao Qiu²  Kang Ning²  Kai Kang²  Hui Chong²  Yuguo Zha²  Yuzheng Dun³ 

[1] Institute for Interdisciplinary Information Sciences, Tsinghua University;Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology;School of Mathematics and Statistics, Huazhong University of Science and Technology;

关键词: Ontology-aware Neural Network (ONN);    Microbial source tracking (MST);    Deep learning;    Ultrafast;    Biomes;   

DOI  :  10.1186/s13073-022-01047-5

来源: DOAJ

【 摘 要 】

Abstract The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST .

【 授权许可】

Unknown