BioCreative V CDR task corpus: a resource for chemical disease relation extraction

Li, Jiao; Sun, Yueping; Johnson, Robin J.; Sciaky, Daniela; Wei, Chih-Hsuan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J.; Wiegers, Thomas C.; Lu, Zhiyong

doi:10.1093/database/baw068

Cited by 526 publications

(407 citation statements)

References 28 publications

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“…Finally, CTD continues its commitment to spearhead and advance biomedical text-mining research for the scientific community by teaming with the National Center for Biotechnology Information (NCBI) to organize a BioCreative community challenge which focused on developing tools to identify and extract specific disease and chemical content (11). For this endeavor, we helped develop a large corpus of manually curated annotations for chemicals, diseases and their interactions from 1500 PubMed articles (12); this corpus is freely available (download by clicking: http://sourceforge.net/projects/bioc/files/CDR_Data.zip/download), as are many of the associated text-mining tools developed by the 25 teams that participated.…”

Section: New Featuresmentioning

confidence: 99%

“…These data are further associated with external data sets to establish novel, statistically ranked inferences between diverse types of information (6–7). Additionally, as part of our continued, active engagement with the scientific community, CTD plays a significant role in advancing text-mining methods for biomedical information as part of the BioCreative consortium (8–12), facilitates the development of semantic standards for the environmental health science community (13), complies with reporting standards set by the BioSharing Information Resources (14) and is a registered member (https://biosharing.org/biodbcore-000173) of BioDBcore (15). …”

Section: Introductionmentioning

confidence: 99%

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The Comparative Toxicogenomics Database: update 2017

et al. 2016

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The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.

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Section: New Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Comparative Toxicogenomics Database: update 2017

et al. 2016

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“…As shown in Table 3, the proposed model has achieved the best performance with the highest F-score 58.8%, which proved that our model was better than a traditional machine learning or rule-based method. 29 UET-CAM firstly resolved co-references by using a multi-pass sieve to identify cross-sentence references for entities and then extracted drug-disease relations by using SVM, which achieved better performance. We only dealt with the second subtask.…”

Section: Results Analysismentioning

confidence: 99%

“…Table 4 shows the performance of different methods on the CID task. 29 UET-CAM firstly resolved co-references by using a multi-pass sieve to identify cross-sentence references for entities and then extracted drug-disease relations by using SVM, which achieved better performance. (UTH-CCB) combined two SVM-based classifiers, which were trained on sentence-level and document-level.…”

Section: 4mentioning

confidence: 99%

A novel deep learning method for extracting unspecific biomedical relation

Bai

Wang

et al. 2018

Concurrency and Computation

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Biomedical relation extraction is an important research subject in Natural language processing (NLP). Deep learning technology has shown greater value in improving accuracy of relation extraction results recently. Existing methods mostly focus on extracting (1) specific relation from short texts (eg, drug-drug interaction and protein-protein interaction) and (2) unspecific relation from full text corpora. However, extracting unspecific relation from short text, which is more and more important in practical use, is rarely studied. In this paper, a new model called MAT-LSTM is proposed to extract unspecific relation from short text in biomedical literatures. Experiments on two Biocreative benchmark datasets and one BioNLP benchmark datasets were made to measure the validity of the proposed model MAT-LSTM, and better performance is achieved. The MAT-LSTM model is also applied practically in extracting unspecific relation contained in the PubMed literatures. The results extracted from PubMed by using the proposed model were verified by experts mostly, indicating the practical value of the MAT-LSTM model. KEYWORDS biomedical relation, deep learning, natural language processing, unspecific relation INTRODUCTIONWith the increasing popularity of precision medicine, the extraction of semantic relation between entities from biomedical literatures has attracted widespread attention in the areas of information extraction and natural language processing. 1 Discovering relations between different biomedical factors (eg, genome, metabolome, and transcriptome) is the foundation to better serve precision medicine. 2 Existing methods to extract specific types of biomedical relations (such as drug-drug interactions 3 and gene-disease interaction 4 ) are mature, whereas a method to extract unspecific types of relation is needed to discover different types relations. In this paper, a model is proposed to extract such unspecific relations.Existing biomedical relation extraction methods, including co-occurrence-based methods, rule-based methods, and machine learning methods, can be classified into two categories: (1) extracting unspecific relations in the literature from the full text and (2) extracting one specific type of relation in the literatures from short text (eg, gene-disease interaction and drug-disease interaction), instead of the full text.(1) Extracting unspecific relations in the literature from the full text.The co-occurrence-based approach is the simplest and most direct one. This method shows two features: first, in the same sentence, the closer the distance between two entities, the greater the correlation; second, the more times the two entities often appear in the same sentence, the greater Concurrency Computat Pract Exper. 2020;32:e5005. wileyonlinelibrary.com/journal/cpe

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“…Spice/herb names in abstracts were tagged using a dictionary matching method. For recognizing and normalizing disease names in the text, we used TaggerOne (Leaman and Lu, 2016) which has a reported precision of 85% and recall of 80% on the Biocreative V Chemical Disease Relation test set (Li et al, 2016). We used the pre-trained disease-only model available with TaggerOne (Leaman and Lu, 2016) on our data.…”

Section: Methodsmentioning

confidence: 99%

SpiceRx: an integrated resource for the health impacts of culinary spices and herbs

Tuwani

Garg

et al. 2018

Preprint

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Spices and herbs are key dietary ingredients used in cuisines across the world. They have been reported to be of medicinal value for a wide variety of diseases through a large body of biomedical investigations. Bioactive phytochemicals in these plant products form the basis of their therapeutic potential as well as adverse effects. A systematic compilation of empirical data involving these aspects of culinary spices and herbs could help unravel molecular mechanisms underlying their effects on health.SpiceRx provides a platform for exploring the health impact of spices and herbs used in food preparations through a structured database of tripartite relationships with their phytochemicals and disease associations. Starting with an extensive dictionary of culinary spices and herbs, their disease associations were text mined from MEDLINE, the largest database of biomedical abstracts, assisted with manual curation. This information was further combined with spice-phytochemical and phytochemical-disease associations. SpiceRx is an integrated repertoire of evidence-based knowledge pertaining to the health impacts of culinary spices and herbs, and facilitates their disease-specific culinary recommendations as well as exploration of molecular mechanisms underlying their health effects. Availability and Implementation:SpiceRx is available at http://cosylab.iiitd.edu.in/spicerx and supports all modern browsers. SpiceRx is implemented with Python web development framework Django and relational database PostgreSQL; the front-end was built using HTML, CSS,

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BioCreative V CDR task corpus: a resource for chemical disease relation extraction

Cited by 526 publications

References 28 publications

The Comparative Toxicogenomics Database: update 2017

The Comparative Toxicogenomics Database: update 2017

A novel deep learning method for extracting unspecific biomedical relation

SpiceRx: an integrated resource for the health impacts of culinary spices and herbs

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