| LitCovid-PD-MONDO-v1 | | PubDictionaries annotation for disease terms - updated at 2020-04-20
It is based on MONDO Version 2020-04-20.
The terms in MONDO are loaded in PubDictionaries, with which the annotations in this project are produced. The parameter configuration used for this project is here.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 13.4 K | | Jin-Dong Kim | 2023-11-29 | Released | |
| LitCoin-entities | | | 13.6 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| GlyCosmos600-FMA | | | 13.8 K | | Jin-Dong Kim | 2024-09-28 | | |
| LitCoin-PubTator-for-Tuning | | A set of randomly selected PubMed articles with PubTator annotation.
The labels of PubTator annotations are converted to corresponding labels for LitCoin as follows:
'Gene' -> 'GeneOrGeneProduct',
'Disease' -> 'DiseaseOrPhenotypicFeature',
'Chemical' -> 'ChemicalEntity'
'Species' -> 'OrganismTaxon'
'Mutation' -> 'SequenceVariant'
'CellLine' -> 'CellLine' | 14.2 K | | Jin-Dong Kim | 2023-11-29 | | |
| bionlp-st-ge-2016-test-ihmc | | | 14.4 K | Lucian Galescu | | 2023-11-29 | Testing | |
| events-check-again | | | 14.4 K | | | 2023-11-30 | Testing | |
| bionlp-st-ge-2016-reference | | It is the benchmark reference data set of the BioNLP-ST 2016 GE task.
It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins.
The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible.
For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test).
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set (this project)
bionlp-st-ge-2016-test: benchmark test data set (annotations are blined)
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. | 14.4 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Released | |
| bionlp-st-ge-2016-reference-tees | | NER and event extraction produced by TEES (with the default GE11 model) for the 20 full papers used in the BioNLP 2016 GE task reference corpus. | 14.6 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| LitCoin-training-merged | | | 14.8 K | | Jin-Dong Kim | 2023-11-24 | | |
| LitCoin-GeneOrGeneProduct-v0 | | https://pubdictionaries.org/text_annotation.json?dictionary=NCBIGene-NER&threshold=0.85&abbreviation=true | 15.8 K | | Jin-Dong Kim | 2023-11-29 | | |
| bionlp-st-ge-2016-uniprot | | UniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test).
The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set).
For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).
| 16.2 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-sentences-v1 | | Sentence segmentation of all the texts in the LitCovid literature. The segmentation is automatically obtained using the TextSentencer annotation service developed and maintained by DBCLS. | 16.5 K | | Jin-Dong Kim | 2023-11-27 | Released | |
| testtesttest | | | 17.4 K | | Jin-Dong Kim | 2024-09-16 | Developing | |
| GO-CC | | Annotation for cellular components as defined in the "Cellular Component" subtree of Gene Ontology | 17.6 K | DBCLS | Jin-Dong Kim | 2023-11-30 | Developing | |
| test-210614 | | | 17.8 K | | Jin-Dong Kim | 2024-01-05 | Testing | |
| preeclampsia_genes | | | 17.8 K | | Jin-Dong Kim | 2023-11-29 | Developing | |
| pubmed-sentences-benchmark | | A benchmark data for text segmentation into sentences.
The source of annotation is the GENIA treebank v1.0.
Following is the process taken.
began with the GENIA treebank v1.0.
sentence annotations were extracted and converted to PubAnnotation JSON.
uploaded. 12 abstracts met alignment failure.
among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639.
among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text.
30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them.
| 18.4 K | GENIA project | Jin-Dong Kim | 2023-11-28 | Released | |
| sonoma | | _ | 19.3 K | Standigm | | 2023-11-24 | Testing | |
| GO-MF | | Annotation for molecular functions as defined in the "Molecular Function" subtree of Gene Ontology | 19.7 K | DBCLS | Jin-Dong Kim | 2023-12-04 | Testing | |
| Nanbyo-330-20171127 | | Disease descriptions extracted from MHLW | 19.8 K | | Toyofumi Fujiwara | 2023-11-26 | Testing | |
