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Yue Wang
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AnEMthe largest manually annotated corpus on anatomical entities2019-04-03
DisGeNET5Associations obtained by text mining MEDLINE abstracts using the BeFree system2019-03-11
PIRProtein Information Resource (PIR)2019-03-12
Projects
NameT Description# Ann.Updated atStatus
1-10 / 25 show all
0_colil781 K2023-11-24
AnEM_full-texts250 documents selected randomly from full-text papers Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation Together with AnEM_abstracts, it is probably the largest manually annotated corpus on anatomical entities.6872023-11-29Uploading
bionlp-st-pc-2013-trainingThe training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013. The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component.7.86 K2023-11-27Released
PennBioIEThe PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short).23.8 K2023-11-26Released
AIMedThe AIMed corpus is one of the most widely used corpora for protein-protein interaction extraction. The protein annotations are either parts of the protein interaction annotations, or are uninvolved in any protein interaction annotation. Publication: http://www.cs.utexas.edu/~ml/papers/bionlp-aimed-04.pdf4.04 K2023-11-27Testing
bionlp-st-cg-2013-trainingThe training dataset from the cancer genetics task in the BioNLP Shared Task 2013. Composed of anatomical and molecular entities.10.9 K2023-11-28Released
PIR-corpus1The Protein Information Resource (PIR) is not biased towards any particular biomedical domain, and is expected to provide more diverse protein names in a given sample size. Annotation category: protein, compound-protein, acronym.4.44 K2023-11-27Released
SCAI-TestA small corpus for the evaluation of dictionaries containing chemical entities. Publication: http://www.scai.fraunhofer.de/fileadmin/images/bio/data_mining/paper/kolarik2008.pdf Original source: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html1.21 K2023-11-28Released
bionlp-st-epi-2011-trainingThe training dataset from the Epigenetics and Post-translational Modifications (EPI) task in the BioNLP Shared Task 2011. The core entities of the task are genes and gene products (RNA and proteins), identified in the data simply as "Protein" annotations. 7.59 K2023-11-29Released
jnlpba-st-trainingThe training data used in the task came from the GENIA version 3.02 corpus, This was formed from a controlled search on MEDLINE using the MeSH terms "human", "blood cells" and "transcription factors". From this search, 1,999 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. For the shared task only the classes protein, DNA, RNA, cell line and cell type were used. The first three incorporate several subclasses from the original taxonomy while the last two are interesting in order to make the task realistic for post-processing by a potential template filling application. The publication year of the training set ranges over 1990~1999.51.1 K2023-11-26Released
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