ArticleNature cancer2024
The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.
Article in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- ESR1 mutations are associated with enhanced MTORC1 signaling in a context-dependent manner.Communications biology · 2026Article
- Decoding neuro-tumor interactions in pancreatic cancer: mechanisms, immunosuppressive networks and therapeutic opportunities.Molecular biomedicine · 2026Review
- The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer.Annals of surgical oncology · 2026Review
- Multi-modal learning with incomplete data.Nature communications · 2026Article
- Beyond sex determination: the Y chromosome in male cancers.Nature reviews. Cancer · 2026Review
- From Association to Prediction: Translational Barriers in Pain Biomarker Research.Journal of personalized medicine · 2026Article
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Molecular subtypes in pancreatic cancer: from academic promise to clinical reality.Molecular cancer · 2026Review
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.Journal of translational medicine · 2026Article
- Strategy for early diagnosis of pancreatic cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- From ISO 9001:2015 to ISO 20387:2018: a proposed implementation roadmap for biobanks.Frontiers in genetics · 2026Article
- Cancer Stem Cells and Medicinal Plants, A Comprehensive Review of their Potential in Tumor Suppression and Recurrence Prevention.Anti-cancer agents in medicinal chemistry · 2026Review
- Real-world evidence on data quality in precision oncology platforms: insights from the Molecular Twin Research Umbrella protocol.Frontiers in digital health · 2026Article
- GAN-enhanced machine learning and metabolic modeling identify reprogramming in pancreatic cancer.PLoS computational biology · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.
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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.