Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-05-05

AUTHORS

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, Ana Rath, Michael Brudno

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. More... »

PAGES

1391-1400

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41436-020-0812-7

DOI

http://dx.doi.org/10.1038/s41436-020-0812-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1127348583

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32366968


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