> top > projects

Projects

NameT Description# Ann.AuthorMaintainerUpdated_atStatus

561-580 / 590 show all
bionlp-st-id-2011-training The training dataset from the infectious diseases (ID) task in the BioNLP Shared Task 2011. Entity types: - Genes and gene products: gene, RNA, and protein name mentions. - Two-component systems: mentions of the names of two-component regulatory systems, frequently embedding the names of the two Proteins forming the system.- Chemicals: mentions of chemical compounds such as "NaCL".- Organisms: mentions of organism names or organism specification through specific properties (e.g. "graRS mutant").- Regulons/Operons: mentions of names of specific regulons and operons.5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2023-11-28Released
bionlp-ost-19-BB-kb-ner-train 3.56 Kldeleger2023-11-28Developing
ENG_RE_mabarros 193mabarros2023-11-26Developing
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 Wei2023-11-24Released
MeasurableQuantitativeAnnotation A collection and annotation the measurable quantity information from 3202 pubmed article, which can be used for the task of extracting measurable quantity information. Annotation category: entity, num, unit.2.84 KWenjieNie2023-11-29Testing
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 KDBCLSJin-Dong Kim2023-11-29Released
Training_Data_Chinese_zh_en 0wmtbio2023-11-29Developing
FA_Top100Plus-GeneProtein Top100＋本来Top100に入るべきだった７レビューの計、107レビュー中101レビュー。 5414, 6076, 6930, 8403, 9643, 18544は、0denotationでドキュメント自体登録していない。 attributesの詳細はconfig参照。 ドキュメントのソースDBが@AikoHIRAKIとなっているものはTypo修正がPubAnnotationの公式FirstAuthorsドキュメントに反映された段階で置き換えます。 10.4 Kyucca2023-11-29Uploading
FA_Top100-Disease 1/2 FirstAuthor Top100 (201811-201910) for diseases MONDO & HPO2.14 KAikoHIRAKI2023-11-29
OryzaGP_2021_v2 OryzaGP_2021_v2 will use a second annotator 208 Klarmande2023-11-29Developing
Test20200205 553BLAH6-Tweets2023-11-29Testing
togotv_exam 12togove_test2023-11-29
FA_108-forWeb 12242,12112, 18829は、0denotationでドキュメント自体登録していない。 @AikoHIRAKIはtypoを修正したレビューフォルダ。50AikoHIRAKI2023-11-29Developing
epi-statement-test 2Jin-Dong Kim2023-11-30Testing
ENG_NER_NEL_pruas 582Pedro Ruaspruas_182023-11-30Developing
ENG_NER_NEL_mabarros 452mabarros2023-12-01Developing
ENG_RE_pruas 248pruas_182023-12-01Developing
test_lasige 494Jin-Dong Kim2023-12-02Testing
CORD-19_HIRAKI HIRAKI Annotation for CORD-192.98 KAikoHIRAKI2023-11-29Testing
PT_NER_NEL_Diana 318dpavot2023-11-24Developing
NameT # Ann.AuthorMaintainerUpdated_atStatus

561-580 / 590 show all
bionlp-st-id-2011-training 5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2023-11-28Released
bionlp-ost-19-BB-kb-ner-train 3.56 Kldeleger2023-11-28Developing
ENG_RE_mabarros 193mabarros2023-11-26Developing
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2023-11-24Released
MeasurableQuantitativeAnnotation 2.84 KWenjieNie2023-11-29Testing
bionlp-st-ge-2016-test 7.99 KDBCLSJin-Dong Kim2023-11-29Released
Training_Data_Chinese_zh_en 0wmtbio2023-11-29Developing
FA_Top100Plus-GeneProtein 10.4 Kyucca2023-11-29Uploading
FA_Top100-Disease 2.14 KAikoHIRAKI2023-11-29
OryzaGP_2021_v2 208 Klarmande2023-11-29Developing
Test20200205 553BLAH6-Tweets2023-11-29Testing
togotv_exam 12togove_test2023-11-29
FA_108-forWeb 50AikoHIRAKI2023-11-29Developing
epi-statement-test 2Jin-Dong Kim2023-11-30Testing
ENG_NER_NEL_pruas 582Pedro Ruaspruas_182023-11-30Developing
ENG_NER_NEL_mabarros 452mabarros2023-12-01Developing
ENG_RE_pruas 248pruas_182023-12-01Developing
test_lasige 494Jin-Dong Kim2023-12-02Testing
CORD-19_HIRAKI 2.98 KAikoHIRAKI2023-11-29Testing
PT_NER_NEL_Diana 318dpavot2023-11-24Developing