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NameTDescription# Ann. AuthorMaintainerUpdated_atStatus

301-320 / 556 show all
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
LitCovid-sample-PD-MONDO 1.21 KJin-Dong Kim2023-11-27Developing
MicrobeTaxon 1.23 KYo Shidahara2023-11-26Testing
LitCovid-sample-UniProt 1.25 KJin-Dong Kim2023-11-30Testing
LitCovid-sample-PD-IDO 1.27 KJin-Dong Kim2023-11-28Beta
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.1.28 KINRAYue Wang2023-11-29Released
bionlp-ost-19-BB-norm-dev 1.33 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-norm-ner-dev 1.33 Kldeleger2023-11-27Developing
BLAH2015_Annotations_test_5 1.34 Knestoralvaronestoralvaro2023-11-30Testing
LitCovid-sample-PD-NCBITaxon 1.35 KJin-Dong Kim2023-11-29Beta
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
LitCovid-sample-CHEBI 1.44 KJin-Dong Kim2023-11-29Testing
Grays_part1 Embryology1.44 Kokubo2023-11-30Testing
funRiceGenes-all 1.51 KPubDictionariesYue Wang2023-11-29Developing
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
parkinson parkinson's disease 1.55 KKyungeunKyungeun2023-11-29Testing
PMA_age_indications 1.57 Ktherightstef2023-11-29Developing
ICD10 Annotation for disease names as defined in ICD101.6 KDBCLSJin-Dong Kim2023-11-29Developing
LitCoin-NCBITaxon-2 1.65 Katsuko2023-11-29Testing
consensus_PMA_Age_Indications 1.7 Klaurenc2023-11-28Beta
NameT# Ann. AuthorMaintainerUpdated_atStatus

301-320 / 556 show all
SCAI-Test 1.21 KCALBC ProjectYue Wang2023-11-28Released
LitCovid-sample-PD-MONDO 1.21 KJin-Dong Kim2023-11-27Developing
MicrobeTaxon 1.23 KYo Shidahara2023-11-26Testing
LitCovid-sample-UniProt 1.25 KJin-Dong Kim2023-11-30Testing
LitCovid-sample-PD-IDO 1.27 KJin-Dong Kim2023-11-28Beta
bionlp-st-bb3-2016-training 1.28 KINRAYue Wang2023-11-29Released
bionlp-ost-19-BB-norm-dev 1.33 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-norm-ner-dev 1.33 Kldeleger2023-11-27Developing
BLAH2015_Annotations_test_5 1.34 Knestoralvaronestoralvaro2023-11-30Testing
LitCovid-sample-PD-NCBITaxon 1.35 KJin-Dong Kim2023-11-29Beta
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2023-11-24Released
LitCovid-sample-CHEBI 1.44 KJin-Dong Kim2023-11-29Testing
Grays_part1 1.44 Kokubo2023-11-30Testing
funRiceGenes-all 1.51 KPubDictionariesYue Wang2023-11-29Developing
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
parkinson 1.55 KKyungeunKyungeun2023-11-29Testing
PMA_age_indications 1.57 Ktherightstef2023-11-29Developing
ICD10 1.6 KDBCLSJin-Dong Kim2023-11-29Developing
LitCoin-NCBITaxon-2 1.65 Katsuko2023-11-29Testing
consensus_PMA_Age_Indications 1.7 Klaurenc2023-11-28Beta