Evidence map›Paper›PMID 32366968›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2020

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Willie H Chang, Pouria Mashouri, Alexander X Lozano, Brittney Johnstone, Mia Husić, Annie Olry, Sylvie Maiella, Tugce B Balci, Sarah L Sawyer, Peter N Robinson and 2 more

Erratum issuedAbstract read
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In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. The Human Phenotype Ontology in 2021.Nucleic acids research · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Willie H ChangCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.
Pouria MashouriCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.
Alexander X LozanoCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.
Brittney JohnstoneCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.
Mia HusićCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.
Annie OlryOrphanet, Institut national de la santé et de la recherche médicale, Paris, France.
Sylvie MaiellaOrphanet, Institut national de la santé et de la recherche médicale, Paris, France.
Tugce B BalciMedical Genetics Program of Southwestern Ontario, London Health Sciences Centre, London, ON, Canada.
Sarah L SawyerDepartment of Genetics, Children's Hospital of Eastern Ontario and Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada.
Peter N RobinsonThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Ana RathOrphanet, Institut national de la santé et de la recherche médicale, Paris, France.
Michael BrudnoCentre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada. brudno@cs.toronto.edu.ORCID http://orcid.org/0000-0001-7947-2243

Funding

CIHR
6 · The paper itself

Abstract

purposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

methodsWe developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data.

resultsDeploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs.

conclusionPhenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

Indexed as

CrowdsourcingHumansKnowledge BasesMachine LearningPhenotypeStudentscrowdsourcingmachine learningmedical educationphenotyperare diseases

Identifiers

PMID32366968

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.