| Virus300 | | 300 abstracts from virology journals annotated with viral proteins and species | 0 | http://aclweb.org/anthology/W/W17/W17-2311.pdf | helencook | 2017-08-07 | Released | |
| geneset_names | | | 0 | | alo33 | 2022-04-26 | Released | |
| Inflammaging | | Inflammation axis | 23.4 M | | alo33 | 2023-11-24 | Released | |
| CORD-19_Custom_license_subset | | The Custom license 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. | 5.08 M | | Jin-Dong Kim | 2023-11-24 | Released | |
| 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 M | IBI Group | Yue Wang | 2023-11-24 | Released | |
| CORD-19-PD-UBERON | | PubDictionaries annotation for UBERON terms - updated at 2020-04-30
It is disease term annotation based on Uberon.
The terms in Uberon are uploaded in PubDictionaries
(Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 1.42 M | | Jin-Dong Kim | 2023-11-24 | Released | |
| PubMed_ArguminSci | | Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci). | 777 K | | zebet | 2023-11-24 | Released | |
| 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 K | Chih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong Lu | Chih-Hsuan Wei | 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 | |
| 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 K | IBI Group | Yue Wang | 2023-11-24 | Released | |
| 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 K | GENIA | Yue Wang | 2023-11-26 | Released | |
| 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/W12-2430 | 1.1 K | Kazusa DNA Research Institute and Database Center for Life Science (DBCLS) | Yue Wang | 2023-11-26 | Released | |
| 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). | 23.8 K | UPenn Biomedical Information Extraction Project | Yue Wang | 2023-11-26 | Released | |
| FSU-PRGE | | A new broad-coverage corpus composed of 3,306 MEDLINE abstracts dealing with gene and protein mentions.
The annotation process was semi-automatic.
Publication: http://aclweb.org/anthology/W/W10/W10-1838.pdf | 59.5 K | CALBC Project | Yue Wang | 2023-11-26 | Released | |
| CORD-19-PD-MONDO | | PubDictionaries annotation for MONDO terms - updated at 2020-04-30
It is disease term annotation based on MONDO.
Version 2020-04-20.
The terms in MONDO are loaded in PubDictionaries, with which the annotations in this project are produced. The parameter configuration used for this project is here.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 6.32 M | | Jin-Dong Kim | 2023-11-27 | Released | |
| LitCovid-PD-FMA-UBERON-v1 | | PubDictionaries annotation for anatomy terms - updated at 2020-04-20
Disease term annotation based on FMA and Uberon. Version 2020-04-20.
The terms in FMA and Uberon are loaded in PubDictionaries
(FMA and
Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here for FMA and
there for Uberon.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 4.3 K | | Jin-Dong Kim | 2023-11-27 | Released | |
| 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 K | | zebet | 2023-11-27 | Released | |
| 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 | |
| craft-sa-dev | | Development data for CRAFT SA shared task. This project contains the development (training) annotations for the Structural Annotation task of the CRAFT Shared Task 2019. This particular set contains token and sentence annotations with tokens linked via dependency relations. These dependency relations were automatically generated using the manually curated CRAFT constituency treebank files as input. | 490 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-27 | Released | |
| 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 K | NaCTeM and KISTI | Yue Wang | 2023-11-27 | Released | |
