ArticleScientific reports2023
DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Interpretable deep learning and radiomics for predicting the occurrence of brain metastases following prophylactic brain irradiation in small cell lung cancer.Translational lung cancer research · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Gut Microbiome Composition and Response to Immune Checkpoint Inhibitors in Melanoma: A Systematic Review.Cureus · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression.Scientific reports · 2025Article
- Review
- Microbiota-centered interventions to boost immune checkpoint blockade therapies.The Journal of experimental medicine · 2025Review
- Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response.Cancer communications (London, England) · 2024Review
- phylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.Bioinformatics (Oxford, England) · 2024Article
- Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.mSystems · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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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.
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Registered trials
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