GAN-GMHI: a generative adversarial network with high discriminative power for microbiome-based disease prediction

Yuxue Li; Gang Xie; Yuguo Zha; Kang Ning

doi:10.1016/j.jgg.2023.03.009

Volume 50 Issue 12

Dec. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2023 > 50(12): 1026-1028

PDF( 674 KB)

GAN-GMHI: a generative adversarial network with high discriminative power for microbiome-based disease prediction

doi: 10.1016/j.jgg.2023.03.009

MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Funds:

This work was partially supported by the National Natural Science Foundation of China (32071465, 31871334, and 31671374) and the National Key R&D Program (2018YFC0910502).

Received Date: 2023-03-08
Accepted Date: 2023-03-13
Publish Date: 2023-03-25

Abstract

FullText(HTML)

References(12)

References

Bergot, A.-S., Giri, R., Thomas, R., 2019. The microbiome and rheumatoid arthritis. Best. Pract. Res. Clin. Rheumatol. 33, 101497.

Goodfellow, I., P.-A.J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014. Generative adversarial nets. Adv. Neural. Inf Process. Syst. 2672-2680.

Gupta, V.K., Kim, M., Bakshi, U., Cunningham, K.Y., Davis, J.M., Lazaridis, K.N., Nelson, H., Chia, N., Sung, J., 2020. A predictive index for health status using species-level gut microbiome profiling. Nat. Commun. 11, 4635.

Kashyap, P.C., Chia, N., Nelson, H., Segal, E., Elinav, E., 2017. Microbiome at the frontier of personalized medicine. Mayo. Clin. Proc. 92, 1855-1864.

Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-r., Raychaudhuri, S., 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods. 16, 1289-1296.

Ling, W., Lu, J., Zhao, N., Lulla, A., Plantinga, A.M., Fu, W., Zhang, A., Liu, H., Song, H., Li, Z., et al., 2022. Batch effects removal for microbiome data via conditional quantile regression. Nat. Commun. 13, 5418.

Stein, C.K., Qu, P., Epstein, J., Buros, A., Rosenthal, A., Crowley, J., Morgan, G., Barlogie, B., 2015. Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat. BMC. Bioinformatics 16, 63.

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W.M., Hao, Y., Stoeckius, M., Smibert, P., Satija, R., 2019. Comprehensive integration of single-cell data. Cell 177, 1888-1902.

Sung, J., Wang, Y., Chandrasekaran, S., Witten, D.M., Price, N.D., 2012. Molecular signatures from omics data: From chaos to consensus. Biotechnol. J. 7, 946-957.

Wang, C., Segal, L.N., Hu, J., Zhou, B., Hayes, R.B., Ahn, J., Li, H., 2022. Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk. Microbiome 10, 121.

Wang, D., Hou, S., Zhang, L., Wang, X., Liu, B., Zhang, Z., 2021. iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks. Genome. Biol. 22, 63.

Wang, Y., Ouyang, M., Gao, X., Wang, S., Fu, C., Zeng, J., He, X., 2020. Phocea, pseudoflavonifractor and lactobacillus intestinalis: three potential biomarkers of gut microbiota that affect progression and complications of obesity-induced type 2 diabetes mellitus. Diabetes. Metab. Syndr. Obes. 13, 835-850.

Relative Articles

Supplements(0)

Cited By

Proportional views