Identification of conclusive association entities in biomedical articles.

BACKGROUND: Conclusive association entities (CAEs) in a biomedical article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specifically involved in the associations concluded in a. Identification of CAEs among candidate entities in the title and the abstract of an article is essential for curation and exploration of conclusive findings in biomedical literature. However, the identification is challenging, as it is difficult to conduct semantic analysis to determine whether an entity is a specific target on which the reported findings are conclusive enough. RESULTS: We investigate how five types of statistical indicators can contribute to prioritizing the candidate entities so that CAEs can be ranked on the top for exploratory analysis. The indicators work on titles and abstracts of articles. They are evaluated by the CAEs designated by biomedical experts to curate entity associations concluded in articles. The indicators have significantly different performance in ranking the CAEs identified by the biomedical experts. Some indicators do not perform well in CAE identification, even though they were used in many techniques for article retrieval and keyword extraction. Learning-based fusion of certain indicators can further improve performance. Most of the articles have at least one of their CAEs successfully ranked at top-2 positions. The CAEs can be visualized to support exploratory analysis of conclusive results on the CAEs. CONCLUSION: With proper fusion of the statistical indicators, CAEs in biomedical articles can be identified for exploratory analysis. The results are essential for the indexing of biomedical articles to support validation of highly related conclusive findings in biomedical literature.

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles.

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.

Identification of highly related references about gene-disease association.

BACKGROUND: Curation of gene-disease associations published in literature should be based on careful and frequent survey of the references that are highly related to specific gene-disease associations. Retrieval of the references is thus essential for timely and complete curation. RESULTS: We present a technique CRFref (Conclusive, Rich, and Focused References) that, given a gene-disease pair < g, d>, ranks high those biomedical references that are likely to provide conclusive, rich, and focused results about g and d. Such references are expected to be highly related to the association between g and d. CRFref ranks candidate references based on their scores. To estimate the score of a reference r, CRFref estimates and integrates three measures: degree of conclusiveness, degree of richness, and degree of focus of r with respect to < g, d>. To evaluate CRFref, experiments are conducted on over one hundred thousand references for over one thousand gene-disease pairs. Experimental results show that CRFref performs significantly better than several typical types of baselines in ranking high those references that expert curators select to develop the summaries for specific gene-disease associations. CONCLUSION: CRFref is a good technique to rank high those references that are highly related to specific gene-disease associations. It can be incorporated into existing search engines to prioritize biomedical references for curators and researchers, as well as those text mining systems that aim at the study of gene-disease associations.