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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Willie H Chang^1,2, Pouria Mashouri¹, Alexander X Lozano^1,3,4

¹Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.

Genetics in Medicine : Official Journal of the American College of Medical Genetics

|May 6, 2020

Summary

Student assignments via Phenotate effectively crowdsourced rare disease (RD) phenotype data, creating valuable structured information for diagnosis and research. This approach enhances the computable RD knowledgebase.

Keywords:

crowdsourcing machine learning medical education phenotype rare diseases

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Structured data is crucial for genetic disorder diagnosis and research.
Information on rare diseases (RDs) is often unstructured, hindering computational analysis.
A need exists to increase the availability of structured RD data.

Purpose of the Study:

To develop a crowdsourcing approach for collecting phenotype information for rare diseases using student assignments.
To create a web application, Phenotate, for crowdsourcing disease phenotype annotations.
To generate composite annotations for diseases using machine learning from student-collected data.

Main Methods:

Developed Phenotate, a web application for crowdsourcing phenotype annotations.
Utilized undergraduate genetics student assignments to collect data.
Applied a machine learning approach to generate composite annotations.
Compared student-sourced annotations with clinical practitioners and gold standard data.

Main Results:

Collected annotations for 22 diseases across five undergraduate genetics courses.
Student-sourced annotations demonstrated strong similarity to gold standards (F-measures 0.584–0.868).
Clinicians achieved comparable accuracy using Phenotate annotations for disease identification.
Generated novel structured annotations for six rare diseases lacking gold standards.

Conclusions:

Phenotate successfully enables crowdsourcing of rare disease phenotype annotations through educational assignments.
The web-based tool offers pedagogical benefits and expands the computable rare disease knowledgebase.
Student participation in Phenotate demonstrates their capability in researching rare diseases.