Dataset Information

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.

ABSTRACT: Comprehensive knowledge of genomic variants in a biological context is key for precision medicine. As next-generation sequencing technologies improve, the amount of literature containing genomic variant data, such as new functions or related phenotypes, rapidly increases. Because numerous articles are published every day, it is almost impossible to manually curate all the variant information from the literature. Many researchers focus on creating an improved automated biomedical natural language processing (BioNLP) method that extracts useful variants and their functional information from the literature. However, there is no gold-standard data set that contains texts annotated with variants and their related functions. To overcome these limitations, we introduce a Biomedical entity Relation ONcology COrpus (BRONCO) that contains more than 400 variants and their relations with genes, diseases, drugs and cell lines in the context of cancer and anti-tumor drug screening research. The variants and their relations were manually extracted from 108 full-text articles. BRONCO can be utilized to evaluate and train new methods used for extracting biomedical entity relations from full-text publications, and thus be a valuable resource to the biomedical text mining research community. Using BRONCO, we quantitatively and qualitatively evaluated the performance of three state-of-the-art BioNLP methods. We also identified their shortcomings, and suggested remedies for each method. We implemented post-processing modules for the three BioNLP methods, which improved their performance.Database URL:http://infos.korea.ac.kr/bronco.

SUBMITTER: Lee K

PROVIDER: S-EPMC4830473 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Publications

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.

Lee Kyubum K Lee Sunwon S Park Sungjoon S Kim Sunkyu S Kim Suhkyung S Choi Kwanghun K Tan Aik Choon AC Kang Jaewoo J

Database : the journal of biological databases and curation 20160413

Comprehensive knowledge of genomic variants in a biological context is key for precision medicine. As next-generation sequencing technologies improve, the amount of literature containing genomic variant data, such as new functions or related phenotypes, rapidly increases. Because numerous articles are published every day, it is almost impossible to manually curate all the variant information from the literature. Many researchers focus on creating an improved automated biomedical natural language ...[more]

PMID: 27074804

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Project description:BackgroundMany new medicines have been derived from natural sources such as plants, which have a long history of being used for disease treatment. Thus, their benefits and side effects have been studied, and plant-related information including plant and disease relations have been accumulated in Medline articles. Because numerous articles are available in Medline and are written in natural language, text-mining is important. However, a corpus of plant and disease relations is not available yet. Thus, we aimed to construct such a corpus.Methods and resultsIn this study, we designed and annotated a plant-disease relations corpus, and proposed a computational model to predict plant-disease relations using the corpus. We categorized plant and disease relations into four types: treatments of diseases, causes of diseases, associations, and negative relations. To construct a corpus of plant-disease relations, we first created its annotation guidelines and randomly selected 200 Medline abstracts. From these abstracts, we identified 1,405 and 1,755 plant and disease mentions, annotated to 105 and 237 unique plant and disease identifiers, respectively. When we selected sentences containing at least one plant and one disease mention, we extracted 878 plant and 1,077 disease entities, which finally generated a corpus of plant-disease relations including 1,309 relations from 199 abstracts. To verify the effectiveness of the corpus, we proposed a convolutional neural network model with the shortest dependency path (SDP-CNN) and applied it to the constructed corpus. The micro F-score with ten-fold cross-validation was found to be 0.764. We also applied the proposed SDP-CNN model to all Medline abstracts. When we measured its performance for 483 randomly selected plant-disease co-occurring sentences, the model showed a precision of 0.707.ConclusionThe plant-disease relations corpus is unique and represents an important resource for biomedical text-mining. The corpus of plant and disease relations is available at http://gcancer.org/pdr/.

Dataset Information

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.

Publications

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.

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