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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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Published on: August 15, 2019

PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.

Ling Luo¹, Shankai Yan¹, Po-Ting Lai¹

¹National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Bioinformatics (Oxford, England)

|January 20, 2021

Summary

PhenoTagger is a novel hybrid approach for recognizing Human Phenotype Ontology (HPO) concepts in biomedical text. It combines dictionary and machine learning methods, achieving competitive performance without manual annotation.

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Area of Science:

Biomedical text mining
Natural Language Processing (NLP)
Computational Biology

Background:

Automatic phenotype concept recognition from unstructured text is challenging.
Existing dictionary-based methods have high precision but low recall.
Machine learning methods require extensive manual annotation, which is costly and time-consuming.

Purpose of the Study:

To develop a hybrid method for recognizing Human Phenotype Ontology (HPO) concepts in biomedical text.
To overcome limitations of existing dictionary-based and machine learning-based approaches.
To create a system that does not require manually annotated training data.

Main Methods:

PhenoTagger combines dictionary-based and machine learning-based methods.
A dictionary is constructed using HPO concepts and synonyms.
A distantly supervised training dataset is automatically generated for a deep learning model.
Dictionary and machine learning predictions are integrated for enhanced performance.

Main Results:

PhenoTagger achieves favorable performance compared to previous methods on HPO corpora.
The method demonstrates generalizability by successfully recognizing disease concepts using the MEDIC ontology.
PhenoTagger achieves competitive performance against state-of-the-art supervised methods without manual annotation.

Conclusions:

PhenoTagger offers an effective hybrid approach for biomedical concept recognition.
The method reduces the dependency on costly manual data annotation.
PhenoTagger provides a valuable tool for advancing biomedical text mining research.