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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 260 show all
LitCovid-PubTator 22.6 KJin-Dong Kim2020-04-21Released
LitCovid-docs A comprehensive literature resource on the subject of Covid-19 is collected by NCBI: https://www.ncbi.nlm.nih.gov/research/coronavirus/ The LitCovid project@PubAnnotation is a collection of the titles and abstracts of the LitCovid dataset, for the people who want to perform text mining analysis. Please note that if you produce some annotation to the documents in this project, and contribute the annotation back to PubAnnotation, it will become publicly available together with contribution from other people. If you want to contribute your annotation to PubAnnotation, please refer to the documentation page: http://www.pubannotation.org/docs/submit-annotation/ The list of the PMID is sourced from here The 6 entries of the following PMIDs could not be included because they were not available from PubMed:32161394, 32104909, 32090470, 32076224, 32161394 32188956, 32238946. Below is a notice from the original LitCovid dataset: PUBLIC DOMAIN NOTICE National Center for Biotechnology Information This software/database is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the author's official duties as a United States Government employee and thus cannot be copyrighted. This software/database is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the software and data, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using this software or data. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the authors in any work or product based on this material : Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 0Jin-Dong Kim2020-04-08Released
PubMed_Structured_Abstracts Sections (zones) as retrieved from PubMed.129 Kzebet2020-03-31Released
LitCovid-ArguminSci Discourse elements for the documents in the LitCovid dataset. Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci)4.9 Kzebet2020-03-25Released
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 1990~1999.51.3 KGENIAYue Wang2020-02-02Released
SPECIES800 SPECIES 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 Jensenevangelos2020-02-02Released
GENIAcorpus multi_cell (1,782) mono_cell (222) virus (2,136) protein_family_or_group (8,002) protein_complex (2,394) protein_molecule (21,290) protein_subunit (942) protein_substructure (129) protein_domain_or_region (1,044) protein_other (97) peptide (521) amino_acid_monomer (784) DNA_family_or_group (332) DNA_molecule (664) DNA_substructure (2) DNA_domain_or_region (39) DNA_other (16) RNA_family_or_group (1,545) RNA_molecule (554) RNA_substructure (106) RNA_domain_or_region (8,237) RNA_other (48) polynucleotide (259) nucleotide (243) lipid (2,375) carbohydrate (99) other_organic_compound (4,113) body_part (461) tissue (706) cell_type (7,473) cell_component (679) cell_line (4,129) other_artificial_source (211) inorganic (258) atom (342) other (21,056) 79.2 KGENIA ProjectYue Wang2020-02-02Released
BioLarkPubmedHPO 228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. For more info, please see Groza et al. "Automatic concept recognition using the human phenotype ontology reference and test suite corpora", 2015.7.24 KTudor Grozasimon2020-02-01Released
PIR-corpus2 The protein tag was used to tag proteins, or protein-associated or -related objects, such as domains, pathways, expression of gene. Annotation guideline: http://pir.georgetown.edu/pirwww/about/doc/manietal.pdf5.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
LocText The manually annotated corpus consists of 100 PubMed abstracts annotated for proteins, subcellular localizations, organisms and relations between them. The focus of the corpus is on annotation of proteins and their subcellular localizations.2.29 KGoldberg et alShrikant Vinchurkar2020-02-01Released
PennBioIE The PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short).23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released
PIR-corpus1 The Protein Information Resource (PIR) is not biased towards any particular biomedical domain, and is expected to provide more diverse protein names in a given sample size. Annotation category: protein, compound-protein, acronym.4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
AnEM_abstracts 250 documents selected randomly from citation abstracts Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation Together with AnEM_full-texts, it is probably the largest manually annotated corpus on anatomical entities.1.95 KNaCTeMYue Wang2020-02-01Released
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 Lee2020-02-01Released
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 Wei2020-01-31Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_sample 874xiajingbo2019-06-30Released
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 Kim2019-04-30Released
123123123 123123123150yaoxinzhi2019-04-12Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 260 show all
LitCovid-PubTator 22.6 KJin-Dong Kim2020-04-21Released
LitCovid-docs 0Jin-Dong Kim2020-04-08Released
PubMed_Structured_Abstracts 129 Kzebet2020-03-31Released
LitCovid-ArguminSci 4.9 Kzebet2020-03-25Released
jnlpba-st-training 51.3 KGENIAYue Wang2020-02-02Released
SPECIES800 3.71 KEvangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensenevangelos2020-02-02Released
GENIAcorpus 79.2 KGENIA ProjectYue Wang2020-02-02Released
BioLarkPubmedHPO 7.24 KTudor Grozasimon2020-02-01Released
PIR-corpus2 5.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
LocText 2.29 KGoldberg et alShrikant Vinchurkar2020-02-01Released
PennBioIE 23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released
PIR-corpus1 4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
AnEM_abstracts 1.95 KNaCTeMYue Wang2020-02-01Released
CoMAGC 1.53 KLee et alHee-Jin Lee2020-02-01Released
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2020-01-31Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_sample 874xiajingbo2019-06-30Released
bionlp-st-ge-2016-test 7.99 KDBCLSJin-Dong Kim2019-04-30Released
123123123 150yaoxinzhi2019-04-12Released