ArticleBioinformatics (Oxford, England)2019
Deep learning with multimodal representation for pancancer prognosis prediction.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 2 of them syntheses that pooled it.
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Who cites it
161 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.Science advances · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.Journal of imaging · 2026Review
- Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.Statistics in medicine · 2026Article
- Multimodal Late-Fusion of Radiomics, Clinical Data, and Demographics Enhances Personalized Survival Prediction in NSCLC.Cancers · 2026Article
- A Unified Information Bottleneck Framework for Multimodal Biomedical Machine Learning.Entropy (Basel, Switzerland) · 2026Review
- A Bilayer Feature Fusion Framework for Pan-Cancer Survival Prediction Based on Multihead Attention and Adaptive Differential Privacy: Model Development and Validation Study.JMIR medical informatics · 2026Article
- SPECTRE: A Multimodal Spectral Transformer for Small Molecule Annotation.Journal of chemical information and modeling · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- A multimodal embedding model for sepsis data representation.NPJ digital medicine · 2026Article
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- scSurv: a deep generative model for single-cell survival analysis.Bioinformatics (Oxford, England) · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
101 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationEstimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type-using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.
resultsWe used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients. AVAILABILITY AND IMPLEMENTATION: https://github.com/gevaertlab/MultimodalPrognosis.
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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.