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NameTDescription# Ann.AuthorMaintainerUpdated_at Status

61-80 / 592 show all
MENA-example2 3Jin-Dong Kim2023-11-24Testing
tees-test Random PMC document used for testing during the development of a RESTful TEES parsing web service.3.39 KNico ColicNico Colic2023-11-24Developing
sonoma _19.3 KStandigm2023-11-24Testing
OryzaGP_2022 41.3 Klarmande2023-11-24
LitCoin-training-merged 14.8 KJin-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
LitCoin-GeneOrGeneProduct-v3 GeneOrGeneProduct after false positive control4.67 KJin-Dong Kim2023-11-24
Covid19_manual_annotation_v2 4.58 KAikoHIRAKI2023-11-24Developing
proj_h_1 6.7 K2023-11-24
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 KIBI GroupYue Wang2023-11-24Released
PubMed-German-test A collection of PubMed abstracts which are written in German0Jin-Dong Kim2023-11-24Developing
PT_NER_NEL_Diana 318dpavot2023-11-24Developing
OryzaGP A dataset for Named Entity Recognition for rice gene29.1 KHuy Do and Pierre LarmandeYue Wang2023-11-24Uploading
PubMed-2017 abstracts published in 2017.0Jin-Dong Kim2023-11-24Developing
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
NameT# Ann.AuthorMaintainerUpdated_at Status

61-80 / 592 show all
MENA-example2 3Jin-Dong Kim2023-11-24Testing
tees-test 3.39 KNico ColicNico Colic2023-11-24Developing
sonoma 19.3 KStandigm2023-11-24Testing
OryzaGP_2022 41.3 Klarmande2023-11-24
LitCoin-training-merged 14.8 KJin-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
LitCoin-GeneOrGeneProduct-v3 4.67 KJin-Dong Kim2023-11-24
Covid19_manual_annotation_v2 4.58 KAikoHIRAKI2023-11-24Developing
proj_h_1 6.7 K2023-11-24
DisGeNET5_variant_disease 144 KIBI GroupYue Wang2023-11-24Released
PubMed-German-test 0Jin-Dong Kim2023-11-24Developing
PT_NER_NEL_Diana 318dpavot2023-11-24Developing
OryzaGP 29.1 KHuy Do and Pierre LarmandeYue Wang2023-11-24Uploading
PubMed-2017 0Jin-Dong Kim2023-11-24Developing
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
Oncogenesis 0Sophie Nam2023-11-26