Dataset Information

BioNOT: a searchable database of biomedical negated sentences.

ABSTRACT: Negated biomedical events are often ignored by text-mining applications; however, such events carry scientific significance. We report on the development of BioNØT, a database of negated sentences that can be used to extract such negated events.Currently BioNØT incorporates ?32 million negated sentences, extracted from over 336 million biomedical sentences from three resources: ?2 million full-text biomedical articles in Elsevier and the PubMed Central, as well as ?20 million abstracts in PubMed. We evaluated BioNØT on three important genetic disorders: autism, Alzheimer's disease and Parkinson's disease, and found that BioNØT is able to capture negated events that may be ignored by experts.The BioNØT database can be a useful resource for biomedical researchers. BioNØT is freely available at http://bionot.askhermes.org/. In future work, we will develop semantic web related technologies to enrich BioNØT.

SUBMITTER: Agarwal S

PROVIDER: S-EPMC3225379 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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BioNØT: a searchable database of biomedical negated sentences.

Agarwal Shashank S Yu Hong H Kohane Issac I

BMC bioinformatics 20111027

<h4>Background</h4>Negated biomedical events are often ignored by text-mining applications; however, such events carry scientific significance. We report on the development of BioNØT, a database of negated sentences that can be used to extract such negated events.<h4>Description</h4>Currently BioNØT incorporates ≈32 million negated sentences, extracted from over 336 million biomedical sentences from three resources: ≈2 million full-text biomedical articles in Elsevier and the PubMed Central, as ...[more]

PMID: 22032181

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Project description:Previous event-related potential (ERP) studies comparing affirmative and negative sentences revealed an N400 for semantically mismatching final words, resulting in a larger N400 for false relative to true affirmative sentences and an opposite effect for negative sentences. Hence, the N400 was independent of the presence of a negation. However, the true negative as well as the false affirmative condition often contained entities or features from different semantic categories and thereby with weak feature overlap, such as e.g., A cat is (not) a saw or Fears are (not) round, which were then compared to true affirmative and false negative sentences containing entities with stronger feature overlap and partially even hyponomy relations, e.g., A cat is (not) an animal or Planets are (not) round. Employing world-knowledge variations, in the current study, we investigate whether increasing the feature overlap between the entities of all conditions leads to similar ERP-patterns as in the previous studies. For this purpose, we use sentences of the following type: George Clooney is (not) an actor vs. George Clooney is (not) a singer where both target words describe a similar profession and thereby function as alternatives to each other. However, in line with the previous studies, we find a truth by polarity interaction, namely, the N400 ERPs are significantly larger for false compared to true affirmative sentences, whereas the effect for negative sentences shows a reversed, though not significant, trend. Overall, the ERP-data suggest that the integration of a negation with the information in its scope is neither fully incremental nor fully delayed, which might be linked to the use of cohyponyms and to the increased feature overlap between alternatives (e.g., actor, singer). Additionally, questionnaire-based rating data show that affirmative sentences are perceived as more natural than negative sentences, and, moreover, that true sentences are perceived as more natural than false sentences, independent of their polarity.

Project description:BackgroundMedical school curricula strives to teach as much material as can be retained in a limited amount of time. A common "gold standard" resource used building curricula are medical objectives suggested by national societies. Unfortunately these objectives suffer from several functional limitations such as limited accessibility to society members, non-searchable formats (such as nested tables or pdf images), and inability to compare and search across societal objectives for redundancy or gaps. The shift towards integrated curriculums in medical school also highlights the need to access suggested content across classical discipline categories.Main bodyWe have codified recommendations from national societies in the United States for medical school objectives in a common tabular format and developed an open access database which can be searched across disciplines and societies. A front end website that allows for searching objectives by keyword while filtering on society or discipline was created. The objectives returned from the initial search can be subsearched by a second term. There is a large range in the format, age, breadth, quantity, and quality of objectives from different societies. Some unique disciplines have overlapping suggested content though most of the content does seem "binnable" by discipline. The choice of metadata for objectives from each given society was also very inconsistent.ConclusionA free and searchable database of medical content to deliver during medical school has been developed with over 13,000 objectives from 18 societies and 22 disciplines at http://data.medobjectives.marian.edu/ . The normalization of the different disciplines' objectives into a common database allows a platform to standardize objectives moving forward. Future work could include adding user accounts to access the database, submission of new objectives, voting up and down suggested objectives, and adding "answers" mapped to objectives. Keyword tagging could allow import of content (e.g. PowerPoints) and outputting of suggested objectives, which would also allow comparison of curriculum across medical schools.

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Project description:BackgroundAdverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks.ResultsTwo annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the "Biomedical & Procedure" dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for "The use of NLP". The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions.ConclusionsThe corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

Dataset Information

BioNOT: a searchable database of biomedical negated sentences.

Publications

BioNØT: a searchable database of biomedical negated sentences.

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