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Automatic concept recognition using the human phenotype ontology reference and test suite corpora.

Tudor Groza¹, Sebastian Köhler², Sandra Doelken²

¹School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa

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Summary

This summary is machine-generated.

Researchers created a new dataset for recognizing human phenotypes in text. This resource aids in developing and evaluating concept recognition tools for biomedical research.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Ontology Engineering

Background:

Concept recognition tools require annotated text corpora for development and evaluation.
Existing corpora primarily focus on genes and proteins, with limited resources for human phenotypes.
Phenotype concept recognition is a growing area in biomedical text mining.

Purpose of the Study:

To present a novel, manually annotated corpus for human phenotype recognition using the Human Phenotype Ontology (HPO).
To develop a standardized test suite for analyzing errors in phenotype concept recognition.
To evaluate the performance of existing phenotype concept recognition tools.

Main Methods:

Manual annotation of text spans from 228 abstracts with HPO concepts by three curators.
Creation of a test suite with 32 case types for error analysis, covering 2164 HPO concepts.
Comprehensive evaluation of three established phenotype concept recognizers (NCBO Annotator, OBO Annotator, Bio-LarK CR).

Main Results:

A unique, harmonized corpus of human phenotype annotations was generated.
A standardized test suite for concept recognition error analysis was developed.
Performance of three phenotype concept recognizers was evaluated against the new corpus and test suite.

Conclusions:

The developed HPO corpus and test suite serve as a valuable reference standard for phenotype concept recognition.
The evaluation provides insights into the capabilities and limitations of current phenotype recognition tools.
These resources will facilitate the advancement of biomedical text mining for human phenotypes.