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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 316 show all
Virus300 300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
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 Wei2023-11-24Released
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 Lee2023-11-24Released
jnlpba-st-training The 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 KGENIAYue Wang2023-11-26Released
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 Wang2023-11-26Released
PennBioIE The 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 KUPenn Biomedical Information Extraction ProjectYue Wang2023-11-26Released
LitCovid-ArguminSci Discourse elements for the documents in the LitCovid dataset. Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci)4.9 Kzebet2023-11-27Released
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 Wang2023-11-27Released
bionlp-st-ge-2016-test-proteins Protein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task. A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations. For more details, please refer to the benchmark test data set (bionlp-st-ge-2016-test). 4.34 KDBCLSJin-Dong Kim2023-11-27Released
PIR-corpus1 The 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 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2023-11-27Released
123123123 123123123150yaoxinzhi2023-11-27Released
CellFinder CellFinder corpus4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2023-11-27Released
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 Wang2023-11-28Released
bionlp-st-ge-2016-coref Coreference annotation to the benchmark data set (reference and test) of BioNLP-ST 2016 GE task. For detailed information, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).853DBCLSJin-Dong Kim2023-11-28Released
SMAFIRA_Feedback_Research_Goal 15zebet2023-11-28Released
2015-BEL-Sample-2 The 295 BEL statements for sample set used for the 2015 BioCreative challenge.11.4 KFabio RinaldiNico Colic2023-11-28Released
PubMed_Structured_Abstracts Sections (zones) as retrieved from PubMed.131 Kzebet2023-11-28Released
SPECIES800 SPECIES 800 (S800): an abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers. Described in: The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.00653903.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2023-11-28Released
bionlp-st-2016-SeeDev-training Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task. SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology. GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events. For more information, please refer to the task website All annotations : Train set Development set Test set (without events) 35EstelleChaix2023-11-28Released
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 Wang2023-11-28Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 316 show all
Virus300 0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2023-11-24Released
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
CyanoBase 1.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2023-11-26Released
PennBioIE 23.8 KUPenn Biomedical Information Extraction ProjectYue Wang2023-11-26Released
LitCovid-ArguminSci 4.9 Kzebet2023-11-27Released
bionlp-st-pc-2013-training 7.86 KNaCTeM and KISTIYue Wang2023-11-27Released
bionlp-st-ge-2016-test-proteins 4.34 KDBCLSJin-Dong Kim2023-11-27Released
PIR-corpus1 4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2023-11-27Released
123123123 150yaoxinzhi2023-11-27Released
CellFinder 4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2023-11-27Released
SCAI-Test 1.21 KCALBC ProjectYue Wang2023-11-28Released
bionlp-st-ge-2016-coref 853DBCLSJin-Dong Kim2023-11-28Released
SMAFIRA_Feedback_Research_Goal 15zebet2023-11-28Released
2015-BEL-Sample-2 11.4 KFabio RinaldiNico Colic2023-11-28Released
PubMed_Structured_Abstracts 131 Kzebet2023-11-28Released
SPECIES800 3.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2023-11-28Released
bionlp-st-2016-SeeDev-training 35EstelleChaix2023-11-28Released
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2023-11-28Released