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NameTDescription# Ann.Author MaintainerUpdated_atStatus

481-500 / 556 show all
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
LappsTest Project to test posting annotations directly from the Language Applications Grid2.67 KKeith Sudermanksuderman2023-11-27Developing
parkinson parkinson's disease 1.55 KKyungeunKyungeun2023-11-29Testing
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
ENG_NER_NEL Annotations in COVID-19 related PubMed abstracts from the following ontologies: Disease Ontology ("do"), Gene Ontology ("go"), Human Phenotype Ontology ("hpo"), ChEBI ontology ("chebi"), MeSH 493LASIGE-DeSTpruas_182023-11-26Developing
PT_NER_NEL Annotations in Portuguese COVID-19 related abstracts from MeSH terminology245LASIGE-DeSTpruas_182023-11-29Developing
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
Biotea NCBO annotation on full text for PMC articles. Currently including only a small set of 2811 articles corresponding to those supporting curated diesease-protein annotation from UniProt and with machine-processable full text.894 KL. Garcia2023-11-24Developing
bionlp-st-ge-2016-test-ihmc 14.4 KLucian Galescu2023-11-29Testing
CellFinder CellFinder corpus4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2023-11-27Released
biosemtest test submitting Peregrine annotations35.6 KMark Thompsonmarkthompson2023-11-29Testing
CHEMDNER-training-test The training subset of the CHEMDNER corpus29.4 KMartin Krallinger et al.Jin-Dong Kim2023-11-27Testing
genia-medco-coref Coreference annotation made to the Genia corpus, following the MUC annotation scheme. It is a product of the collaboration between the Genia and the MedCo projects.45.9 KMedCo project & Genia projectJin-Dong Kim2023-11-24Developing
LODQA 0Michel Dumontiermicheldumontier2015-02-20Testing
ICU_characters 286ming-qi-wang2023-11-27
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.1.91 KNaCTeMYue Wang2023-11-29Released
AnEM_full-texts 250 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.687NaCTeMYue Wang2023-11-29Uploading
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-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
BLAH2015_Annotations_test_5 1.34 Knestoralvaronestoralvaro2023-11-30Testing
NameT# Ann.Author MaintainerUpdated_atStatus

481-500 / 556 show all
CyanoBase 1.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2023-11-26Released
LappsTest 2.67 KKeith Sudermanksuderman2023-11-27Developing
parkinson 1.55 KKyungeunKyungeun2023-11-29Testing
KYMEKA20240117Test 0Kyung-Min ChaeKyung-Min Chae2024-01-17Testing
ENG_NER_NEL 493LASIGE-DeSTpruas_182023-11-26Developing
PT_NER_NEL 245LASIGE-DeSTpruas_182023-11-29Developing
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
Biotea 894 KL. Garcia2023-11-24Developing
bionlp-st-ge-2016-test-ihmc 14.4 KLucian Galescu2023-11-29Testing
CellFinder 4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2023-11-27Released
biosemtest 35.6 KMark Thompsonmarkthompson2023-11-29Testing
CHEMDNER-training-test 29.4 KMartin Krallinger et al.Jin-Dong Kim2023-11-27Testing
genia-medco-coref 45.9 KMedCo project & Genia projectJin-Dong Kim2023-11-24Developing
LODQA 0Michel Dumontiermicheldumontier2015-02-20Testing
ICU_characters 286ming-qi-wang2023-11-27
AnEM_abstracts 1.91 KNaCTeMYue Wang2023-11-29Released
AnEM_full-texts 687NaCTeMYue Wang2023-11-29Uploading
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2023-11-28Released
bionlp-st-pc-2013-training 7.86 KNaCTeM and KISTIYue Wang2023-11-27Released
BLAH2015_Annotations_test_5 1.34 Knestoralvaronestoralvaro2023-11-30Testing