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• W4328126040 abstract "Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment. Understanding how microbiota affects individual responses is crucial for precision oncology. However, it is challenging to identify key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. Moreover, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response. DeepGeni not only improves microbiome-driven prediction of immune checkpoint inhibitor responsiveness but also suggests potential microbial targets for fecal microbiota transplant or probiotics improving the outcome of cancer immunotherapy." @default.
• W4328126040 created "2023-03-22" @default.
• W4328126040 creator A5069553088 @default.
• W4328126040 creator A5077875574 @default.
• W4328126040 date "2023-03-21" @default.
• W4328126040 modified "2023-09-26" @default.
• W4328126040 title "DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy" @default.
• W4328126040 cites W2019607817 @default.
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• W4328126040 doi "https://doi.org/10.1038/s41598-023-31210-w" @default.
• W4328126040 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36944780" @default.
• W4328126040 hasPublicationYear "2023" @default.
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