| PGR-UNK | | Identification of Unknown Relations
| 91 | Diana Sousa | dpavot | 2023-11-29 | Developing | |
| biomarkers | | IL/TNF biomarkers | 857 K | | alo33 | 2023-11-24 | | |
| Inflammaging | | Inflammation axis | 23.4 M | | alo33 | 2023-11-24 | Released | |
| 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 K | Lee et al | Hee-Jin Lee | 2023-11-24 | Released | |
| KoreanFN-example | | Korean FrameNet example | 6 | | Jin-Dong Kim | 2023-11-29 | Developing | |
| PA-LLM | | 🖐️ LLMs for biomedical text summarisation | 0 | | Nico Colic | 2024-01-19 | Developing | |
| PMA_Manual | | Manually annotated examples of medical device PMA approval statements | 204 | Stefano Rensi | therightstef | 2023-11-27 | Developing | |
| LitCoin-Disease-MeSH | | MeSH C and F03, plus false negatives that appear in two or more documents in LitCoin-entities | 3.56 K | | yucca | 2023-11-29 | Testing | |
| 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)
| 78.9 K | GENIA Project | Yue Wang | 2023-11-29 | Released | |
| bayaba | | nalee | 7 | | Nakyolee | 2023-11-29 | | |
| LitCovid-PubTatorCentral | | Named-entities for the documents in the LitCovid dataset. Annotations were automatically predicted by the PubTatorCentral tool (https://www.ncbi.nlm.nih.gov/research/pubtator/) | 4.64 K | | zebet | 2023-11-27 | Released | |
| GlycoBiology-NCBITAXON | | NCBITAXON-based annotation to GlycoBiology abstracts | 32.7 K | | shuo50 | 2023-11-29 | Testing | |
| 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 K | L. Garcia | | 2023-11-24 | Developing | |
| bionlp-st-ge-2016-test-tees | | NER and event extraction produced by TEES (with the default GE11 model) for the 14 full papers used in the BioNLP 2016 GE task test corpus. | 9.17 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| bionlp-st-ge-2016-reference-tees | | NER and event extraction produced by TEES (with the default GE11 model) for the 20 full papers used in the BioNLP 2016 GE task reference corpus. | 14.6 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| EDAN70 | | NLP tagging of articles concerning covid19. | 0 | | fettmedknaoz | 2023-11-29 | | |
| tagtog | | OpenAccess annotations coming from tagtog.net | 0 | tagtog | tagtog | 2015-02-23 | Developing | |
| PubCasesORDO | | ORDO annotation in PubCases | 865 K | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| OryzaGP_2021_v2 | | OryzaGP_2021_v2 will use a second annotator | 208 K | | larmande | 2023-11-29 | Developing | |
| OryzaGP_2020 | | OryzaGP is a dataset of pubmed abstract related to Oryza sativa species | 0 | Pierre Larmande | larmande | 2023-11-29 | Developing | |
