| LitCovid-PD-HP-v1 | | PubDictionaries annotation for human phenotype terms - updated at 2020-04-20
Disease term annotation based on HP.
Version 2020-04-20.
The terms in HP 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. | 3.03 K | | Jin-Dong Kim | 2023-11-29 | Released | |
| Glycan-Motif | | | 11.1 K | | ISSAKU YAMADA | 2023-11-29 | Testing | |
| EwhaLecture2020 | | testing | 0 | | | 2023-11-29 | Testing | |
| Find2ER | | Find the Findings of Enzymatic Reaction | 0 | Akihiro Kameda | Akihiro Kameda | 2023-11-26 | Testing | |
| metamap-sample | | Sample annotation of MetaMep, produced by Aronson, et al.
An overview of MetaMap: historical perspective and recent advances, JAMIA 2010 | 10.9 K | Alan R Aronson | Jin-Dong Kim | 2023-11-27 | Testing | |
| relna | | https://www.tagtog.net/jmcejuela/relna | 2.77 K | Ashish Baghudana and Juan Miguel Cejuela | tagtog | 2023-11-29 | Developing | |
| QFMC_MEDLINE | | Quaero French Medical Corpus:
Annotation of MEDLINE titles | 5.9 K | Aurélie Névéol | Pierre Zweigenbaum | 2023-11-29 | Beta | |
| SNPPhenoExt | | | 3 | behrouz bokharaeian | bokharaeian | 2023-11-29 | Developing | |
| BioASQ-sample | | collection of PubMed articles which appear in the BioASQ sample data set. | 0 | BioASQ | Jin-Dong Kim | 2023-11-28 | Testing | |
| 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 | |
| SCAI-Test | | A small corpus for the evaluation of dictionaries containing chemical entities.
Publication: http://www.scai.fraunhofer.de/fileadmin/images/bio/data_mining/paper/kolarik2008.pdf
Original source: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html | 1.21 K | CALBC Project | Yue Wang | 2023-11-28 | Released | |
| PathNERGold | | Gold standard used for training PathNER | 0 | Chengkin Wu | nikolamilosevic | 2023-11-29 | Developing | |
| 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 | |
| WikiPainGoldStandard | | Wiki-pain contains molecular interactions that are relevant to pain. | 878 | Daniel Jamieson | nikolamilosevic | 2023-11-28 | Developing | |
| 新着論文レビュー | | 新着論文レビューに関するアノテーション。 | 0 | Database Center for Life Science | Yasunori Yamamoto | 2019-02-04 | Developing | |
| Allie | | An annotation set of abbreviations and expanded forms extracted from PubMed/MEDLINE by machines. | 8.7 M | Database Center for Life Science | Yasunori Yamamoto | 2023-11-24 | Developing | |
| bionlp-st-ge-2016-reference | | It is the benchmark reference data set of the BioNLP-ST 2016 GE task.
It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins.
The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible.
For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test).
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set (this project)
bionlp-st-ge-2016-test: benchmark test data set (annotations are blined)
bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set
Following is supporting resources:
bionlp-st-ge-2016-coref: coreference annotation
bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs.
pmc-enju-pas: dependency parsing result produced by Enju
UBERON-AE: annotation for anatomical entities as defined in UBERON
ICD10: annotation for disease names as defined in ICD10
GO-BP: annotation for biological process names as defined in GO
GO-CC: annotation for cellular component names as defined in GO
A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql. | 14.4 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Released | |
| UBERON-AE | | Annotation for anatomical entities based on the "Anatomical Entity" subtree of UBERON ontology.
Annotations are automatically produced using PubDictionaries with threshold: 0.85. | 859 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Developing | |
| pmc-enju-pas | | Predicate-argument structure annotation produced by Enju.
This data set is initially produced as a supporting resource for BioNLP-ST 2016 GE task.
As so, it currently includes the 34 full paper articles that are in the benchmark data sets of GE 2016 task, reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test), but will be extended to include more papers from the PubMed Central Open Access subset (PMCOA).
| 205 K | DBCLS | Jin-Dong Kim | 2023-11-28 | Developing | |
| bionlp-st-ge-2016-test | | It is the benchmark test data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 14 full paper articles which are about NFκB proteins. For testing purpose, however, annotations are all blinded, which means users cannot see the annotations in this project. Instead, annotations in any other project can be compared to the hidden annotations in this project, then the annotations in the project will be automatically evaluated based on the comparison.
A participant of GE task can get the evaluation of his/her result of automatic annotation, through following process:
Create a new project.
Import documents from the project, bionlp-st-2016-test-proteins to your project.
Import annotations from the project, bionlp-st-2016-test-proteins to your project.
At this point, you may want to compare you project to this project, the benchmark data set. It will show that protein annotations in your project is 100% correct, but other annotations, e.g., events, are 0%.
Produce event annotations, using your system, upon the protein annotations.
Upload your event annotations to your project.
Compare your project to this project, to get evaluation.
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set
bionlp-st-ge-2016-test: benchmark test data set (this project)
bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set
Following is supporting resources:
bionlp-st-ge-2016-coref: coreference annotation
bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs.
pmc-enju-pas: dependency parsing result produced by Enju
UBERON-AE: annotation for anatomical entities as defined in UBERON
ICD10: annotation for disease names as defined in ICD10
GO-BP: annotation for biological process names as defined in GO
GO-CC: annotation for cellular component names as defined in GO
A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql. | 7.99 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Released | |
