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A new synonym-substitution method to enrich the human phenotype ontology

Overview of attention for article published in BMC Bioinformatics, October 2017
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Title
A new synonym-substitution method to enrich the human phenotype ontology
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1858-7
Pubmed ID
Authors

Maria Taboada, Hadriana Rodriguez, Ranga C. Gudivada, Diego Martinez

Abstract

Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem. This paper presents an automated synonym-substitution method to enrich the Human Phenotype Ontology (HPO) with new synonyms. The approach is mainly based on both the lexical properties of the terms and the hierarchical structure of the ontology. By scanning the lexical difference between a term and its descendant terms, the method can learn new names and modifiers in order to generate synonyms for the descendant terms. By searching for the exact phrases in MEDLINE, the method can automatically rule out illogical candidate synonyms. In total, 745 new terms were identified. These terms were indirectly evaluated through the concept annotations on a gold standard corpus and also by document retrieval on a collection of abstracts on hereditary diseases. A moderate improvement in the F-measure performance on the gold standard corpus was observed. Additionally, 6% more abstracts on hereditary diseases were retrieved, and this percentage was 33% higher if only the highly informative concepts were considered. A synonym-substitution procedure that leverages the HPO hierarchical structure works well for a reliable and automatic extension of the terminology. The results show that the generated synonyms have a positive impact on concept recognition, mainly those synonyms corresponding to highly informative HPO terms.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 27%
Student > Master 2 13%
Student > Bachelor 2 13%
Professor 2 13%
Other 1 7%
Other 3 20%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 5 33%
Biochemistry, Genetics and Molecular Biology 2 13%
Medicine and Dentistry 2 13%
Neuroscience 2 13%
Agricultural and Biological Sciences 1 7%
Other 1 7%
Unknown 2 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 October 2017.
All research outputs
#9,595,056
of 11,991,714 outputs
Outputs from BMC Bioinformatics
#3,632
of 4,364 outputs
Outputs of similar age
#199,737
of 273,653 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 111 outputs
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We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.