BackgroundNumerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text.ResultsWe test the idea on two information extraction tasks of Genome-Wide Association Studies (GWAS). The first task is to extract target phenotypes (diseases or traits) of a study and the second is to extract ethnicity backgrounds of study subjects for different stages (initial or replication). Experimental results show that our approach can achieve 87 % of Precision-at-2 (P@2) for disease/trait extraction, and 0.83 of F1-Score for stage-ethnicity extraction, both outperforming their cost-insensitive baseline counterparts.ConclusionsThe results show that curated biomedical databases can potentially be reused as training examples to train information extractors without expert annotation or refinement, opening an unprecedented opportunity of using “big data” in biomedical text mining.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0844-1) contains supplementary material, which is available to authorized users.

Osteoma is a benign osteogenic lesion which is composed of well differentiated mature compact and/or cancellous bone that proliferates continuously. Its prevalence is 4%. Its pathogenesis is still controversial. Solitary peripheral osteoma of craniofacial region is a rare finding. We report a case of 30-year-old female having solitary peripheral osteoma present on the lingual cortex of the left posterior mandible which was initially asymptomatic but now is causing discomfort while chewing and not associated with Gardner's syndrome. We also laid emphasis on its clinical, differential diagnosis, radiological, surgical, and histopathological features. The aim of this paper is to present an uncommon case of solitary peripheral osteoma in the mandible along with analysis of literature for peripheral osteomas of jaws and to contribute to the knowledge concerning the pathogenesis, differential diagnosis, and management of these lesions.

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Background. There is huge amount of full-text biomedical literatures available in public repositories like PubMed Central (PMC). However, a substantial number of the papers are in Portable Document Format (PDF) and do not provide plain text format ready for text mining and natural language processing (NLP). Although there exist many PDF-to-text converters, they still suffer from several challenges while processing biomedical PDFs, such as the correct transcription of titles/abstracts, segmenting references/acknowledgements, special characters, jumbling errors (the wrong order of the text), and word boundaries. Methods. In this paper, we present bioPDFX, a novel tool which complements weaknesses with strengths of multiple state-of-the-art methods and then applies machine learning methods to address all issues above Results. The experiment results on publications of Genome Wide Association Studies (GWAS) demonstrated that bioPDFX significantly improved the quality of XML comparing to state-of-the-art PDF-to-XML converter, leading to a biomedical database more suitable for text mining. Discussion. Overall, the whole pipeline developed in this paper makes the published literature in form of PDF files much better suited for text mining tasks, while slightly improving the overall text quality as well. The service is open to access freely at URL: http://textmining.ucsd.edu:9000 . A list of PubMed Central IDs of the 941 articles (see Supplemental File 1) used in this study is available for download at the same URL. The instructions of how to run the service with a PubMed ID are described in Supplemental File 2.