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NameTDescription # Ann.AuthorMaintainerUpdated_atStatus

561-580 / 590 show all
GlycoConjugate-collection The PubMed entries (titles and abstracts) from the journal of GlycoConjugate0Jin-Dong Kim2023-11-28Developing
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
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. 7.59 KGENIAYue Wang2023-11-29Released
bionlp-st-id-2011-training The training dataset from the infectious diseases (ID) task in the BioNLP Shared Task 2011. Entity types: - Genes and gene products: gene, RNA, and protein name mentions. - Two-component systems: mentions of the names of two-component regulatory systems, frequently embedding the names of the two Proteins forming the system.- Chemicals: mentions of chemical compounds such as "NaCL".- Organisms: mentions of organism names or organism specification through specific properties (e.g. "graRS mutant").- Regulons/Operons: mentions of names of specific regulons and operons.5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2023-11-28Released
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-gro-2013-training The training data set of the BioNLP-ST 2013 GRO task, including 150 MEDLINE abstracts that are annotated with concepts and relations of the Gene Regulation Ontology (GRO; http://www.ebi.ac.uk/Rebholz-srv/GRO/GRO.html)8.02 KJung-jae KimJung-jae Kim2023-11-29Testing
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
CHEMDNER-training-test The training subset of the CHEMDNER corpus29.4 KMartin Krallinger et al.Jin-Dong Kim2023-11-27Testing
NEUROSES This corpus is composed of PubMed articles containing cognitive enhancers and anti-depressants drug mentions. The selected sentences are automatically annotated using the NCBO Annotator with the Chemical Entities of Biological Interest (CHEBI) and Phenotypic Quality Ontology (PATO) ontologies, we also produced annotations using PhenoMiner ontology via a dictionary-based tagger.2.14 Mnestoralvaro2023-11-24Beta
Ab3P-abbreviations This corpus was developed during the creation of the Ab3P abbreviation definition identification tool. It includes 1250 manually annotated MEDLINE records. This gold standard includes 1221 abbreviation-definition pairs. Abbreviation definition identification based on automatic precision estimates Sunghwan Sohn, Donald C Comeau, Won Kim and W John Wilbur BMC Bioinformatics20089:402 DOI: 10.1186/1471-2105-9-4022.33 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2023-11-29Beta
medical_relation This is about medical inner relation.0ruleryangruleryang2023-11-29Testing
Zoonoses This is a main data sets of Zoonoses project used by PanZoora.10.3 KAikoHIRAKI2023-11-26Developing
Zoonoses_partialAnnotation This is a part of Zoonoses project used by PanZoora. But Zoonoses project provides whole manual annotated data but this is partial ones.266AikoHIRAKI2023-11-27Released
KYMEKA20240117Test This is a project to express the linking of terms and ontologies (DOID, FMA, Radlex) used in the dataset 'Annotationdata_type-2' used in BLAH8_Radiological Causal Annotation.0Kyung-Min ChaeKyung-Min Chae2024-01-17Testing
youworks-test this is test annotation.0Hisato Terada2023-11-29Testing
SPECIES800_autotagged This project comprises the SPECIES800 corpus documents automatically annotated by the Jensenlab tagger. Annotated entity types are: Genes/proteins from the mentioned organisms (and any human ones) PubChem Compound identifiers NCBI Taxonomy entries Gene Ontology cellular component terms BRENDA Tissue Ontology terms Disease Ontology terms Environment Ontology terms The SPECIES 800 (S800) comprises 800 PubMed abstracts. In its original form species mentions were manually 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.0065390. The manually annotated corpus is also available as a PubAnnotation project (see here). 0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
LitCoin-GeneOrGeneProduct-v2 threshold = 0.938.98 KJin-Dong Kim2023-11-29
FA_Top100Plus-GeneProtein Top100＋本来Top100に入るべきだった７レビューの計、107レビュー中101レビュー。 5414, 6076, 6930, 8403, 9643, 18544は、0denotationでドキュメント自体登録していない。 attributesの詳細はconfig参照。 ドキュメントのソースDBが@AikoHIRAKIとなっているものはTypo修正がPubAnnotationの公式FirstAuthorsドキュメントに反映された段階で置き換えます。 10.4 Kyucca2023-11-29Uploading
Trait_curation150825 Trait curation0Sachiko_ShirasawaSachiko Shirasawa2023-11-29Testing
Nalee trial version 1Nakyolee2023-11-28
NameT# Ann.AuthorMaintainerUpdated_atStatus

561-580 / 590 show all
GlycoConjugate-collection 0Jin-Dong Kim2023-11-28Developing
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2023-11-28Released
bionlp-st-epi-2011-training 7.59 KGENIAYue Wang2023-11-29Released
bionlp-st-id-2011-training 5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2023-11-28Released
bionlp-st-pc-2013-training 7.86 KNaCTeM and KISTIYue Wang2023-11-27Released
bionlp-st-gro-2013-training 8.02 KJung-jae KimJung-jae Kim2023-11-29Testing
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
CHEMDNER-training-test 29.4 KMartin Krallinger et al.Jin-Dong Kim2023-11-27Testing
NEUROSES 2.14 Mnestoralvaro2023-11-24Beta
Ab3P-abbreviations 2.33 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2023-11-29Beta
medical_relation 0ruleryangruleryang2023-11-29Testing
Zoonoses 10.3 KAikoHIRAKI2023-11-26Developing
Zoonoses_partialAnnotation 266AikoHIRAKI2023-11-27Released
KYMEKA20240117Test 0Kyung-Min ChaeKyung-Min Chae2024-01-17Testing
youworks-test 0Hisato Terada2023-11-29Testing
SPECIES800_autotagged 0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
LitCoin-GeneOrGeneProduct-v2 8.98 KJin-Dong Kim2023-11-29
FA_Top100Plus-GeneProtein 10.4 Kyucca2023-11-29Uploading
Trait_curation150825 0Sachiko_ShirasawaSachiko Shirasawa2023-11-29Testing
Nalee 1Nakyolee2023-11-28