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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 392 show all
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
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 Wang2020-02-01Released
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).23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released
LocText The manually annotated corpus consists of 100 PubMed abstracts annotated for proteins, subcellular localizations, organisms and relations between them. The focus of the corpus is on annotation of proteins and their subcellular localizations.2.29 KGoldberg et alShrikant Vinchurkar2020-02-01Released
PIR-corpus2 The protein tag was used to tag proteins, or protein-associated or -related objects, such as domains, pathways, expression of gene. Annotation guideline: http://pir.georgetown.edu/pirwww/about/doc/manietal.pdf5.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
BioLarkPubmedHPO 228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. For more info, please see Groza et al. "Automatic concept recognition using the human phenotype ontology reference and test suite corpora", 2015.7.24 KTudor Grozasimon2020-02-01Released
DisGeNET5_variant_disease The file contains variant-disease associations obtained by text mining MEDLINE abstracts using the BeFree system, including the variant and disease off sets. 144 KIBI GroupYue Wang2020-02-01Released
DisGeNET5_gene_disease The file contains gene-disease associations obtained by text mining MEDLINE abstracts using the BeFree system including the gene and disease off sets.2.04 MIBI GroupYue Wang2020-02-02Released
GENIAcorpus multi_cell (1,782) mono_cell (222) virus (2,136) protein_family_or_group (8,002) protein_complex (2,394) protein_molecule (21,290) protein_subunit (942) protein_substructure (129) protein_domain_or_region (1,044) protein_other (97) peptide (521) amino_acid_monomer (784) DNA_family_or_group (332) DNA_molecule (664) DNA_substructure (2) DNA_domain_or_region (39) DNA_other (16) RNA_family_or_group (1,545) RNA_molecule (554) RNA_substructure (106) RNA_domain_or_region (8,237) RNA_other (48) polynucleotide (259) nucleotide (243) lipid (2,375) carbohydrate (99) other_organic_compound (4,113) body_part (461) tissue (706) cell_type (7,473) cell_component (679) cell_line (4,129) other_artificial_source (211) inorganic (258) atom (342) other (21,056) 79.2 KGENIA ProjectYue Wang2020-02-02Released
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 Jensenevangelos2020-02-02Released
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 1990~1999.51.3 KGENIAYue Wang2020-02-02Released
CORD-19_All_docs All the documents in the whole CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.0Jin-Dong Kim2020-03-23Released
CORD-19_bioRxiv_medRxiv_subset The bioRxiv/medRxiv subset of the CORD-19 dataset: pre-prints that are not peer reviewed. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT. 0Jin-Dong Kim2020-03-23Released
CORD-19_Commercial_use_subset The Commercial use subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.0Jin-Dong Kim2020-03-23Released
CORD-19_Non-commercial_use_subset The Non commercial use subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.0Jin-Dong Kim2020-03-23Released
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 Kzebet2020-03-25Released
PubMed_Structured_Abstracts Sections (zones) as retrieved from PubMed.131 Kzebet2020-03-31Released
PubMed_ArguminSci Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci).777 Kzebet2020-03-31Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 392 show all
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
PIR-corpus1 4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
PennBioIE 23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released
LocText 2.29 KGoldberg et alShrikant Vinchurkar2020-02-01Released
PIR-corpus2 5.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
BioLarkPubmedHPO 7.24 KTudor Grozasimon2020-02-01Released
DisGeNET5_variant_disease 144 KIBI GroupYue Wang2020-02-01Released
DisGeNET5_gene_disease 2.04 MIBI GroupYue Wang2020-02-02Released
GENIAcorpus 79.2 KGENIA ProjectYue Wang2020-02-02Released
SPECIES800 3.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2020-02-02Released
jnlpba-st-training 51.3 KGENIAYue Wang2020-02-02Released
CORD-19_All_docs 0Jin-Dong Kim2020-03-23Released
CORD-19_bioRxiv_medRxiv_subset 0Jin-Dong Kim2020-03-23Released
CORD-19_Commercial_use_subset 0Jin-Dong Kim2020-03-23Released
CORD-19_Non-commercial_use_subset 0Jin-Dong Kim2020-03-23Released
LitCovid-ArguminSci 4.9 Kzebet2020-03-25Released
PubMed_Structured_Abstracts 131 Kzebet2020-03-31Released
PubMed_ArguminSci 777 Kzebet2020-03-31Released