ArticleNPJ digital medicine2025
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
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Who cites it
25 citing papers in PubMed.
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- A Multimodal Biomedical Transformer Fusion Network for Disease-Level Rare-Disease-Inheritance Classification Using Ontology-Enriched Text, Metadata, and Gene Associations.Biomedicines · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Article
- Genetic Diagnosis and Discovery Enabled by Large Language Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI for scientific discovery is a social problem.Patterns (New York, N.Y.) · 2026Review
- Article
- Article
- Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.PLOS digital health · 2026Article
- A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis.Methods and protocols · 2026Review
- Article
- Evaluating algorithmic approaches to rare disease case-finding: a retrospective validation study using electronic health records.Orphanet journal of rare diseases · 2026Article
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- Data-augmented machine learning refines the effective-concentration estimate for eculizumab in complement-mediated diseases.Frontiers in immunology · 2026Article
- Referral route: a determinant of inequity for children with undiagnosed genetic diseases?Frontiers in genetics · 2026Article
- AI-driven enhancements in rare disease diagnosis and support system optimization.Intractable & rare diseases research · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
There are over 7000 rare diseases, some affecting 3500 or fewer patients in the United States. Due to clinicians' limited experience with such diseases and the heterogeneity of clinical presentations, ~70% of individuals seeking a diagnosis remain undiagnosed. Deep learning has demonstrated success in aiding the diagnosis of common diseases. However, existing approaches require labeled datasets with thousands of diagnosed patients per disease. We present SHEPHERD, a few-shot learning approach for multi-faceted rare disease diagnosis. SHEPHERD performs deep learning over a knowledge graph enriched with rare disease information and is trained on a dataset of simulated rare disease patients. We demonstrate SHEPHERD's effectiveness across diverse diagnostic tasks, performing causal gene discovery, retrieving "patients-like-me", and characterizing novel disease presentations, using real-world cohorts from the Undiagnosed Diseases Network (N = 465), MyGene2 (N = 146), and the Deciphering Developmental Disorders study (N = 1431). SHEPHERD demonstrates the potential of knowledge-grounded deep learning to accelerate rare disease diagnosis.
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.