ArticlemSystems2024
Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.
Article in mSystems, 2024. 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.
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Systematic evaluation of TCGA tumor microbiota reveals context-dependent reliability.mSystems · 2026Article
- Analysis of intratumoural bacteria: Opportunities and challenges for cancer therapy.Acta pharmaceutica Sinica. B · 2026Review
- Multiple programmed cell death patterns predict the prognosis and therapeutic sensitivity in head and neck squamous cell carcinoma.Discover oncology · 2026Article
- Integrative multi-omics and mendelian randomization reveal the critical role of pyroptosis in prognosis and therapy of lung squamous cell carcinomas.Frontiers in cell and developmental biology · 2026Article
- Microbiome and cancer: mechanistic insights, diagnostic potential, and therapeutic strategies.Frontiers in cell and developmental biology · 2026Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Integrative pan-cancer analysis of intratumoral microbial communities for biomarker discovery and diagnostic modeling.Journal of translational medicine · 2025Article
- Decoding cancer prognosis with deep learning: the ASD-cancer framework for tumor microenvironment analysis.mSystems · 2025Article
- ADAM-1: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration.IEEE access : practical innovations, open solutions · 2025Article
- Tumor-associated bacteria activate PRDX1-driven glycolysis to promote immune evasion and PD-1 antibody resistance in hepatocellular carcinoma.Frontiers in microbiology · 2025Article
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5 authors.
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Abstract
The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiome and host gene expressions in tumor microenvironment, as well as their concert effects on patient survival. In this study, we aimed to decode this complex relationship by developing ASD-cancer (autoencoder-based subtypes detector for cancer), a semi-supervised deep learning framework that could extract survival-related features from tumor microbiome and transcriptome data, and identify patients' survival subtypes. By using tissue samples from The Cancer Genome Atlas database, we identified two statistically distinct survival subtypes across all 20 types of cancer Our framework provided improved risk stratification (e.g., for liver hepatocellular carcinoma, [LIHC], log-rank test,
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.