ArticlePloS one2013
Mining skeletal phenotype descriptions from scientific literature.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A new synonym-substitution method to enrich the human phenotype ontology.BMC bioinformatics · 2017Article
- The digital revolution in phenotyping.Briefings in bioinformatics · 2016Article
- Automatic concept recognition using the human phenotype ontology reference and test suite corpora.Database : the journal of biological databases and curation · 2015Article
- Concept selection for phenotypes and diseases using learn to rank.Journal of biomedical semantics · 2015Article
- Toward knowledge support for analysis and interpretation of complex traits.Genome biology · 2013Review
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Authors and funding
3 authors.
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Abstract
Phenotype descriptions are important for our understanding of genetics, as they enable the computation and analysis of a varied range of issues related to the genetic and developmental bases of correlated characters. The literature contains a wealth of such phenotype descriptions, usually reported as free-text entries, similar to typical clinical summaries. In this paper, we focus on creating and making available an annotated corpus of skeletal phenotype descriptions. In addition, we present and evaluate a hybrid Machine Learning approach for mining phenotype descriptions from free text. Our hybrid approach uses an ensemble of four classifiers and experiments with several aggregation techniques. The best scoring technique achieves an F-1 score of 71.52%, which is close to the state-of-the-art in other domains, where training data exists in abundance. Finally, we discuss the influence of the features chosen for the model on the overall performance of the method.
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