ArticleNature machine intelligence2023
Multimodal data fusion for cancer biomarker discovery with deep learning.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
164 citing papers in PubMed, 1 synthesis or guideline pooled it, 317 citations in OpenAlex.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- Prognostic significance of systemic immunoinflammatory biomarkers and pathological features in clear cell renal cell carcinoma: a multicenter study.Annals of medicine · 2026Trial
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.Briefings in bioinformatics · 2026Review
- Deep integration of clinical metadata with [European journal of nuclear medicine and molecular imaging · 2026Article
- Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratory measurements, and treatment history.Briefings in bioinformatics · 2026Article
- Multi-modal learning with incomplete data.Nature communications · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
- A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.Science advances · 2026Article
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Metabolism as the biochemical language for mechanistic biomedical AI.Nature metabolism · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- MIND: multimodal integration with neighbourhood-aware distributions.Nature communications · 2026Article
- Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.NPJ breast cancer · 2026Article
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
- A hierarchical clinical fusion transformer model for personalized opioid treatment: Development and validation in diabetic surgical patients.medRxiv : the preprint server for health sciences · 2026Article
104 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
7 authors at 1 institution in 1 country.
Funding
Abstract
Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.
Identifiers
What Socratic holds
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 Socratic graph.