TY - GEN
T1 - An Integrated Multi-Omics Approach for AMR Phenotype Prediction of Gut Microbiota
AU - Gao, Pei
AU - Chen, Zheng
AU - Wang, Dong
AU - Huang, Ming
AU - Ono, Naoaki
AU - Altaf-Ul-Amin, Md
AU - Kanaya, Shigehiko
N1 - Funding Information:
This work was supported by the Next Generation Interdisciplinary Research Project of NAIST and Ministry of Education, Culture, Sports, Science, and Technology of Japan (20K12043 and 16K07223) and NAIST Big Data Project and was partially supported by the Platform Project for Supporting Drug Discovery and Life Science Research funded by the Japan Agency for Medical Research and Development and the National Bioscience Database Center in Japan.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The gut microbiota is crucial for human physiology and susceptibility to diseases. Knowing the AMR phenotype canfacilitate the understanding of the impact of antibiotics administration on the gut microbiota. Nowadays, whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is widely used in clinical microbiology to predict the AMR phenotype. To release the limitations of the genomic information and improve the WGS-AST prediction, we propose an integrated multi-omics approach, employing a deep generative neural network (VAE: variational auto-encoder). We evaluate the proposed approach by two machine learning techniques (i.e., K-means for clustering and Random Forest for classification). Our evaluation results show that the integrated multi-omics approach achieves relatively better performance than the conventional WGS-AST. Moreover, the integrated multi-omics approach is able to visually reveal AMR phenotype of the gutmicrobiota via antibacterial spectrum. Our work provides evidence that multi-omics information is useful to enhance the WGS-AST prediction.
AB - The gut microbiota is crucial for human physiology and susceptibility to diseases. Knowing the AMR phenotype canfacilitate the understanding of the impact of antibiotics administration on the gut microbiota. Nowadays, whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is widely used in clinical microbiology to predict the AMR phenotype. To release the limitations of the genomic information and improve the WGS-AST prediction, we propose an integrated multi-omics approach, employing a deep generative neural network (VAE: variational auto-encoder). We evaluate the proposed approach by two machine learning techniques (i.e., K-means for clustering and Random Forest for classification). Our evaluation results show that the integrated multi-omics approach achieves relatively better performance than the conventional WGS-AST. Moreover, the integrated multi-omics approach is able to visually reveal AMR phenotype of the gutmicrobiota via antibacterial spectrum. Our work provides evidence that multi-omics information is useful to enhance the WGS-AST prediction.
UR - http://www.scopus.com/inward/record.url?scp=85125196807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125196807&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669397
DO - 10.1109/BIBM52615.2021.9669397
M3 - Conference contribution
AN - SCOPUS:85125196807
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 2211
EP - 2216
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -
