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Jin-Dong Kim
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Collections
NameDescriptionUpdated at
11-13 / 13 show all
Glycosmos6This collection contains annotation projects which target all the PubMed abstracts (at the time of January 14, 2022) from the 6 glycobiology-related journals: Glycobiology Glycoconjugate journal The Journal of biological chemistry Journal of proteome research Journal of proteomics Carbohydrate research 2023-11-16
Lectin-Jamboree2024-12-19
GlyCosmos15Collection of annotations to the abstracts from the following journals: Analytical_Chemistry Biochim_Biophys_Acta Carbohydrate_Research Cell Glycobiology Glycoconjugate_Journal J_Am_Chem_Soc Journal_of_Biological_Chemistry Journal_of_Proteome_Research Journal_of_Proteomics Molecular_and_Cellular_Proteomics Nature_Biotechnology Nature_Communications Nature_Methods Scientific_Reports 2024-12-19
Projects
NameTDescription# Ann.Updated atStatus
111-120 / 175 show all
LitCovid-PD-FMA-UBERON1.3 M2023-11-28Developing
NGLY1-deficiencyA collection of PubMed abstracts that may be related to NGLY1 deficiency.60.5 K2023-11-29Developing
preeclampsia_genes17.8 K2023-11-29Developing
Preeclampsia-compare67.2 K2023-11-29Testing
GO-CCAnnotation for cellular components as defined in the "Cellular Component" subtree of Gene Ontology17.6 K2023-11-30Developing
GlyCosmos15-GlycoEpitope19.4 K2024-12-01Developing
pubmed-enju-pasAnnotating PubMed abstracts for predicate-argument structure (PAS). Enju 2.4.2 is used to automatically compute PAS.19.1 M2023-11-24Developing
bionlp-st-ge-2016-reference-eval4262023-11-29Testing
LitCovid-PD-GlycoEpitope9992023-11-29Developing
bionlp-st-ge-2016-testIt 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 K2023-11-29Released
Automatic annotators
NameDescription
1-10 / 40 show all
PubTator-ChemicalTo pull the pre-computed chemical annotation from PubTator.
PubTator-GeneTo pull the pre-computed gene annotation from PubTator.
PubTator-SpeciesTo pull the pre-computed Species annotation from PubTator.
PubTator-DiseaseTo pull the pre-computed disease annotation from PubTator.
PubTator-MutationTo pull the pre-computed mutation annotation from PubTator.
discourse-simplifierA discourse analyzer developed by Univ. Manchester.
PD-NGLY1-deficiency-BA batch annotator for NGLY1 deficiency
PD-UBERON-AEIt annotates for anatomical entities, based on the UBERON-AE dictionary on PubDictionaries. Threshold is set to 0.85.
PD-MONDOPubDictionaries annotation with the MONDO dictionary.
PD-FMA-PAEPhysical Anatomical Entities from FMA
Editors
NameDescription
1-1 / 1
TextAEThe official stable version of TextAE.