ArticleClinical cancer research : an official journal of the American Association for Cancer Research2018
Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06245694 (Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits), which is not on this map. Cited by 507 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
Who cites it
507 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.The Yale journal of biology and medicine · 2023Trial
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Radical Antegrade Modular Pancreatosplenectomy Versus Standard Resection in T3 Body and Tail Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Cohort Study.Annals of surgical oncology · 2026Article
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.International journal of molecular sciences · 2026Article
- Consistency of feature attribution in deep learning architectures for multi-omics.Scientific reports · 2026Article
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring, and Tissue Microenvironment.International journal of molecular sciences · 2026Review
- Microbial Signatures in Head and Neck versus Gastrointestinal Tumors: Identification and Prognostic Modeling.Polish journal of microbiology · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- WDHD1 promotes hepatocellular carcinoma progression by affecting the cell cycle and immune evasion.Translational oncology · 2026Article
- A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.Scientific reports · 2026Article
447 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)-based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients' data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences (
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Registered trials
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.