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NameTDescription# Ann.AuthorMaintainerUpdated_at Status

21-40 / 316 show all
TextMining-in-bioinformatics TextMining in bioinformatics0Aohnewug2020-04-21Developing
BLAH2021-glytoucan-iupac 0kiyoko2021-01-19
SMAFIRA_Feedback_Labels 0zebet2021-01-21Developing
vtt_friends_s01e07 0donghwan kim2023-11-24
LitCovid-PD-MONDO 2.26 MJin-Dong Kim2023-11-24
0_colil 781 KYue Wang2023-11-24
test10 212Jin-Dong Kim2023-11-24
bionlp-ost-19-BB-rel-ner-test 125ldeleger2023-11-24Developing
genia-medco-coref Coreference annotation made to the Genia corpus, following the MUC annotation scheme. It is a product of the collaboration between the Genia and the MedCo projects.45.9 KMedCo project & Genia projectJin-Dong Kim2023-11-24Developing
Trait curation Project for trait curation in PGDBj479Sachiko ShirasawaSachiko Shirasawa2023-11-24Testing
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
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 Lee2023-11-24Released
proj_h_1 6.7 K2023-11-24
PT_NER_NEL_Diana 318dpavot2023-11-24Developing
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 KGENIAYue Wang2023-11-26Released
Oncogenesis 0Sophie Nam2023-11-26
kaiyin_test 3.33 Kzhoukaiyin2023-11-26
updated_tagging_age_PMA_annotations 1.94 Klaurenc2023-11-26Developing
MicrobeTaxon 1.23 KYo Shidahara2023-11-26Testing
KAIST_NLP_Annotation11 4.88 Kkaist_nlp2023-11-26Developing
NameT# Ann.AuthorMaintainerUpdated_at Status

21-40 / 316 show all
TextMining-in-bioinformatics 0Aohnewug2020-04-21Developing
BLAH2021-glytoucan-iupac 0kiyoko2021-01-19
SMAFIRA_Feedback_Labels 0zebet2021-01-21Developing
vtt_friends_s01e07 0donghwan kim2023-11-24
LitCovid-PD-MONDO 2.26 MJin-Dong Kim2023-11-24
0_colil 781 KYue Wang2023-11-24
test10 212Jin-Dong Kim2023-11-24
bionlp-ost-19-BB-rel-ner-test 125ldeleger2023-11-24Developing
genia-medco-coref 45.9 KMedCo project & Genia projectJin-Dong Kim2023-11-24Developing
Trait curation 479Sachiko ShirasawaSachiko Shirasawa2023-11-24Testing
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2023-11-24Released
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
proj_h_1 6.7 K2023-11-24
PT_NER_NEL_Diana 318dpavot2023-11-24Developing
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
Oncogenesis 0Sophie Nam2023-11-26
kaiyin_test 3.33 Kzhoukaiyin2023-11-26
updated_tagging_age_PMA_annotations 1.94 Klaurenc2023-11-26Developing
MicrobeTaxon 1.23 KYo Shidahara2023-11-26Testing
KAIST_NLP_Annotation11 4.88 Kkaist_nlp2023-11-26Developing