ReviewNature methods2023
Machine learning in rare disease.
Review in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses 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
58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Systematic review of prediction models and meta-analysis of risk factors for invasive fungal infection in children.BMJ open · 2026Pooled it
- Integrative transcriptomics and hypothesis-driven transfer machine learning reveal conserved and species-specific host-parasite dynamics acrossFrontiers in cellular and infection microbiology · 2026Pooled it
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.medRxiv : the preprint server for health sciences · 2026Article
- Wafer-scalable artificial synapses and logic gate circuits based on coplanar self-aligned-gate organic transistors for artificial vision applications.Microsystems & nanoengineering · 2026Article
- Persona-Driven Data Augmentation for Disease Name Recognition Across Rare and General Disease Corpora: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- A novel biological function-based method for mining core genes in rare disease with limited cases.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- An evaluation of variable selection methods in competing risks with one rare event: a simulation study.BMC medical research methodology · 2026Article
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphoma.HemaSphere · 2026Article
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- Targeted Next-Generation Sequencing of the Leptin-Melanocortin Pathway in Severe Obesity.Obesity (Silver Spring, Md.) · 2026Article
- Artificial intelligence chatbots in response to patient's common inquiries about chordoma: A cross-sectional study.Brain & spine · 2026Article
- Machine Learning Is Not Just for Prediction: Its Role as an Exploratory Analytical Tool in Medicine.Annals of vascular diseases · 2026Review
- IL18 Works Like a Two-Side Coin in Acute Pancreatitis.International journal of general medicine · 2026Article
- Exploratory machine learning analysis to characterize angioscopic features associated with atherosclerosis-related aortic dissection: an exploratory single-center angioscopic study.Frontiers in cardiovascular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.