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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 276 show all
NCBIDiseaseCorpus The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06Released
CyanoBase Cyanobacteria are prokaryotic organisms that have served as important model organisms for studying oxygenic photosynthesis and have played a significant role in the Earthfs history as primary producers of atmospheric oxygen. Publication: http://www.aclweb.org/anthology/W12-24301.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2016-05-17Released
2015-BEL-Sample-2 The 295 BEL statements for sample set used for the 2015 BioCreative challenge.11.4 KFabio RinaldiNico Colic2016-05-25Released
bionlp-st-epi-2011-training The 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. 7.6 KGENIAYue Wang2016-12-06Released
bionlp-st-cg-2013-training The training dataset from the cancer genetics task in the BioNLP Shared Task 2013. Composed of anatomical and molecular entities.10.9 KNaCTeMYue Wang2016-12-06Released
SCAI-Test A 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 KCALBC ProjectYue Wang2017-04-03Released
bionlp-st-bb3-2016-training Entity (bacteria, habitats and geographical places) annotation to the training dataset of the BioNLP-ST 2016 BB task. For more information, please refer to bionlp-st-bb3-2016-development and bionlp-st-bb3-2016-test. Bacteria Bacteria entities are annotated as contiguous spans of text that contains a full unambiguous prokaryote taxon name, the type label is Bacteria. The Bacteria type is a taxon, at any taxonomic level from phylum (Eubacteria) to strain. The category that the text entities have to be assigned to is the most specific and unique category of the NCBI taxonomy resource. In case a given strain, or a group of strains is not referenced by NCBI, it is assigned with the closest taxid in the taxonomy. Habitat Habitat entities are annotated as spans of text that contains a complete mention of a potential habitat for bacteria, the type label is Habitat. Habitat entities are assigned one or several concepts from the habitat subpart of the OntoBiotope ontology. The assigned concepts are as specific as possible. OntoBiotope defines most relevant microorganism habitats from all areas considered by microbial ecology (hosts, natural environment, anthropized environments, food, medical, etc.). Habitat entities are rarely referential entities, they are usually noun phrases including properties and modifiers. There are rare cases of habitats referred with adjectives or verbs. The spans are generally contiguous but some of them are discontinuous in order to cope with conjunctions. Geographical Geographical entities are geographical and organization places denoted by official names.1.29 KINRAYue Wang2017-05-22Released
Virus300 300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed-sentences-benchmark A benchmark data for text segmentation into sentences. The source of annotation is the GENIA treebank v1.0. Following is the process taken. began with the GENIA treebank v1.0. sentence annotations were extracted and converted to PubAnnotation JSON. uploaded. 12 abstracts met alignment failure. among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639. among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text. 30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them. 18.5 KGENIA projectJin-Dong Kim2017-08-15Released
bionlp-st-pc-2013-training The 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 KNaCTeM and KISTIYue Wang2017-08-28Released
Wangshuguang HZAU_bioinformatics_competition603wangshuguangwangshuguang2018-04-03Released
RDoCTask2SampleData Each annotation file contains an annotated abstract with the most relevant sentence. The relevant sentence is annotated with the RDoC category name. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/. 10mmanani1s2019-03-25Released
RDoCTask1SampleData Each annotation file contains an annotated abstract with an RDoC category. Each title span in these sample data is annotated with the corresponding related RDoC construct, although the RDoC category would apply for the entire abstract. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/.20mmanani1s2019-03-25Released
123123123 123123123150yaoxinzhi2019-04-12Released
AGAC_sample 874xiajingbo2019-06-30Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
tmVarCorpus Wei C-H, Harris BR, Kao H-Y, Lu Z (2013) tmVar: A text mining approach for extracting sequence variants in biomedical literature, Bioinformatics, 29(11) 1433-1439, doi:10.1093/bioinformatics/btt156.1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2020-01-31Released
CoMAGC In order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations. In order to support the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences, we publish CoMAGC, a corpus with multi- faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, the corpus deals with changes in gene expression levels among other types of gene changes.1.53 KLee et alHee-Jin Lee2020-02-01Released
AnEM_abstracts 250 documents selected randomly from citation abstracts 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_full-texts, it is probably the largest manually annotated corpus on anatomical entities.1.95 KNaCTeMYue Wang2020-02-01Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 276 show all
NCBIDiseaseCorpus 6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06Released
CyanoBase 1.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2016-05-17Released
2015-BEL-Sample-2 11.4 KFabio RinaldiNico Colic2016-05-25Released
bionlp-st-epi-2011-training 7.6 KGENIAYue Wang2016-12-06Released
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2016-12-06Released
SCAI-Test 1.21 KCALBC ProjectYue Wang2017-04-03Released
bionlp-st-bb3-2016-training 1.29 KINRAYue Wang2017-05-22Released
Virus300 0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed-sentences-benchmark 18.5 KGENIA projectJin-Dong Kim2017-08-15Released
bionlp-st-pc-2013-training 7.86 KNaCTeM and KISTIYue Wang2017-08-28Released
Wangshuguang 603wangshuguangwangshuguang2018-04-03Released
RDoCTask2SampleData 10mmanani1s2019-03-25Released
RDoCTask1SampleData 20mmanani1s2019-03-25Released
123123123 150yaoxinzhi2019-04-12Released
AGAC_sample 874xiajingbo2019-06-30Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2020-01-31Released
CoMAGC 1.53 KLee et alHee-Jin Lee2020-02-01Released
AnEM_abstracts 1.95 KNaCTeMYue Wang2020-02-01Released