Evidence map›Paper›PMID 29895857›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019

Novel phenotype-disease matching tool for rare genetic diseases.

Jing Chen, Huan Xu, Anil Jegga, Kejian Zhang, Pete S White, Ge Zhang

Abstract readComparative StudyEvaluation Study
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In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Review
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  12. The role of phenotype-based search approaches using public online databases in diagnostics of Mendelian disorders.Genetics in medicine : official journal of the American College of Medical Genetics · 2021
    Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Jing ChenDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. jing.chen2@cchmc.org.
Huan XuDivision of Biostatistics and Bioinformatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Anil JeggaDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Kejian ZhangDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Pete S WhiteDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Ge ZhangDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. ge.zhang@cchmc.org.

Funding

Better Outcomes for Children: Promoting Excellence in Healthcare Genomics to Inform PolicyU01HG008666 · NHGRI · CINCINNATI CHILDRENS HOSP MED CTR · PI HARLEY, JOHN BARKER · 2015 to 2019
$4.2M
6 · The paper itself

Abstract

purposeTo improve the accuracy of matching rare genetic diseases based on patient's phenotypes.

methodsWe introduce new methods to prioritize diagnosis of genetic diseases based on integrated semantic similarity (method 1) and ontological overlap (method 2) between the phenotypes expressed by a patient and phenotypes annotated to known diseases.

resultsWe evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records. We demonstrated that the two methods achieved significantly improved performance compared with previous methods in correctly prioritizing candidate diseases in all of the three sets. Our methods are freely available as a web application ( https://gddp. RESEARCH: cchmc.org/ ) to aid diagnosis of genetic diseases.

conclusionOur methods can capture the diagnostic information embedded in the phenotype ontology, consider all phenotypes exhibited by a patient, and are more robust than the existing methods when phenotypes are incorrectly or imprecisely specified. These methods can assist the diagnosis of rare genetic diseases and help the interpretation of the results of DNA tests.

Indexed as

SoftwareBiological OntologiesComputational BiologyComputer SimulationElectronic Health RecordsGenetic Diseases, InbornHumansInternetPhenotypeRare DiseasesSoftware ValidationDiagnosisHuman Phenotype OntologyMendelian disease

Identifiers

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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.