Identifying genotype-phenotype relationships in biomedical text

• Published in: Journal of biomedical semantics Vol. 8; no. 1; pp. 57 - 16
• Main Authors: Khordad, Maryam, Mercer, Robert E.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.12.2017; BioMed Central; BMC
• Subjects: Computational linguistics; Genotype-phenotype relationship; Genotypes; Machine learning; Medical research; Natural language processing; Phenotypes; Self-training; Semi-automatic corpus annotation; Semi-automatic corpus annotation; Genotype-phenotype relationship; Phenotypes; Computational linguistics; Self-training; Genotypes
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-017-0163-8

One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify this information for future curation is essential. Such a system provides important and up to date data for database construction and updating, and even text summarization. In this paper we present a machine learning method to identify these genotype-phenotype relationships. No large human-annotated corpus of genotype-phenotype relationships currently exists. So, a semi-automatic approach has been used to annotate a small labelled training set and a self-training method is proposed to annotate more sentences and enlarge the training set. The resulting machine-learned model was evaluated using a separate test set annotated by an expert. The results show that using only the small training set in a supervised learning method achieves good results (precision: 76.47, recall: 77.61, F-measure: 77.03) which are improved by applying a self-training method (precision: 77.70, recall: 77.84, F-measure: 77.77). Relationships between genotypes and phenotypes is biomedical information pivotal to the understanding of a patient's situation. Our proposed method is the first attempt to make a specialized system to identify genotype-phenotype relationships in biomedical literature. We achieve good results using a small training set. To improve the results other linguistic contexts need to be explored and an appropriately enlarged training set is required.