ArticleNature communications2019
Personalised analytics for rare disease diagnostics.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 27 citations in OpenAlex.
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Performance of ChatGPT-4o and Four Open-Source Large Language Models in Generating Diagnoses Based on China's Rare Disease Catalog: Comparative Study.Journal of medical Internet research · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization.Genetics in medicine : official journal of the American College of Medical Genetics · 2025Article
- Genomes in clinical care.NPJ genomic medicine · 2024Review
- Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.Bioinformatics (Oxford, England) · 2023Article
- Utility of tissue-specific gene expression scores for gene prioritization in Mendelian diseases.Journal of human genetics · 2022Article
- Phenotype-aware prioritisation of rare Mendelian disease variants.Trends in genetics : TIG · 2022Review
- Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease.Human mutation · 2022Review
- Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study.NPJ digital medicine · 2021Article
- A domestic cat whole exome sequencing resource for trait discovery.Scientific reports · 2021Article
- Pharmacogenomics applied to recombinant human growth hormone responses in children with short stature.Reviews in endocrine & metabolic disorders · 2021Review
- CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores.Genome medicine · 2021Article
- The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner.
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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.