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NameTDescription# Ann.AuthorMaintainer Updated_atStatus

1-20 / 185 show all
Find2ERFind the Findings of Enzymatic Reaction0Akihiro KamedaAkihiro Kameda2015-02-20Testing
AlvisNLP-TestProject for testing AlviNLP PubAnnotation server during BLAH3.17Bibliome2017-01-20Testing
SNPPhenoExt3behrouz bokharaeianbokharaeian2016-04-30Developing
PreeclampsiaA collection of titles and abstracts of "Preeclampsia"-related papers. They were extracted from PubMed using the MeSH term "Preeclampsia" and specifying the language to be "English, on 11th September, 2017. The texts were then annotated by PubDictionaries using the dictionary "Preeclampsia".94.3 Kcallahan_tiff2017-11-28Developing
NCBIDiseaseCorpusThe NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06Released
Test_PubTator62Chih-Hsuan Wei2016-04-20Testing
tmVarCorpusWei 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 Wei2015-08-06Released
Ab3P-abbreviationsThis corpus was developed during the creation of the Ab3P abbreviation definition identification tool. It includes 1250 manually annotated MEDLINE records. This gold standard includes 1221 abbreviation-definition pairs. Abbreviation definition identification based on automatic precision estimates Sunghwan Sohn, Donald C Comeau, Won Kim and W John Wilbur BMC Bioinformatics20089:402 DOI: 10.1186/1471-2105-9-4022.34 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2016-07-29Beta
DLUT931DLUT NLP Lab.Test our event extration result for 16 GE task.4.57 KDLUT9312016-05-17Testing
test010Erika Asamizu2015-09-11Testing
Erin_test@ Yonsei University0ErinErinHJ_Kim2017-07-13Testing
SPECIES800SPECIES 800 (S800): an abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers. Described in: The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.00653903.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2015-11-20Released
SPECIES800_autotaggedThis project comprises the SPECIES800 corpus documents automatically annotated by the Jensenlab tagger. Annotated entity types are: Genes/proteins from the mentioned organisms (and any human ones) PubChem Compound identifiers NCBI Taxonomy entries Gene Ontology cellular component terms BRENDA Tissue Ontology terms Disease Ontology terms Environment Ontology terms The SPECIES 800 (S800) comprises 800 PubMed abstracts. In its original form species mentions were manually identified and mapped to the corresponding NCBI Taxonomy identifiers. Described in: The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390. The manually annotated corpus is also available as a PubAnnotation project (see here). 0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
2015-BEL-SampleAn attempt to upload 295 BEL statements, i.e. the sample set used for the 2015 BioCreative challenge. 58Fabio RinaldiFabio Rinaldi2015-02-26Testing
test50glennq2016-02-06
TA test (NLP)0hahmyg2015-09-11Testing
KAIST_NLP_2015_fall195jeonguk kimhahmyg2015-09-18Testing
Staphylococcus7.46 Kharuoharuo2016-04-06Testing
CoGe_Citation_AnnotationsAnnotated PMC abstracts+full articles, that cite the "CoGe" papers (PMID: 18952863, 18269575). Total Num Citations: 165 Total Num Unique Citations: 141 Total Num Abstracts: 165 Total Num Whole Articles: 165 0Heather Lenthclent2016-10-11Uploading
CoMAGCIn 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 Lee2015-02-24Released
NameT# Ann.AuthorMaintainer Updated_atStatus

1-20 / 185 show all
Find2ER0Akihiro KamedaAkihiro Kameda2015-02-20Testing
AlvisNLP-Test17Bibliome2017-01-20Testing
SNPPhenoExt3behrouz bokharaeianbokharaeian2016-04-30Developing
Preeclampsia94.3 Kcallahan_tiff2017-11-28Developing
NCBIDiseaseCorpus6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06Released
Test_PubTator62Chih-Hsuan Wei2016-04-20Testing
tmVarCorpus1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2015-08-06Released
Ab3P-abbreviations2.34 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2016-07-29Beta
DLUT9314.57 KDLUT9312016-05-17Testing
test010Erika Asamizu2015-09-11Testing
Erin_test0ErinErinHJ_Kim2017-07-13Testing
SPECIES8003.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2015-11-20Released
SPECIES800_autotagged0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
2015-BEL-Sample58Fabio RinaldiFabio Rinaldi2015-02-26Testing
test50glennq2016-02-06
TA test (NLP)0hahmyg2015-09-11Testing
KAIST_NLP_2015_fall195jeonguk kimhahmyg2015-09-18Testing
Staphylococcus7.46 Kharuoharuo2016-04-06Testing
CoGe_Citation_Annotations0Heather Lenthclent2016-10-11Uploading
CoMAGC1.53 KLee et alHee-Jin Lee2015-02-24Released