id stringclasses 5
values | document_id stringclasses 5
values | passages list | entities list | events list | coreferences list | relations list |
|---|---|---|---|---|---|---|
18162134 | 18162134 | [
{
"id": "18162134__text",
"type": "full_text",
"text": [
"BackgroundMuch of our current knowledge of the molecular expression profile of human embryonic stem cells (hESCs) is based on transcriptional approaches. These analyses are only partly predictive of protein expression however, and do not sh... | [
{
"id": "18162134_T1",
"type": "GeneProtein",
"text": [
"MST3"
],
"offsets": [
[
23576,
23580
]
],
"normalized": []
},
{
"id": "18162134_T2",
"type": "GeneProtein",
"text": [
"PRKCB1"
],
"offsets": [
[
22722,
... | [] | [] | [] |
18286199 | 18286199 | [{"id":"18286199__text","type":"full_text","text":["BackgroundHuman embryonic stem cells (hESCs) off(...TRUNCATED) | [{"id":"18286199_T1","type":"Species","text":["human"],"offsets":[[24042,24047]],"normalized":[]},{"(...TRUNCATED) | [] | [] | [] |
16316465 | 16316465 | [{"id":"16316465__text","type":"full_text","text":["BackgroundUsing antibodies to specific protein a(...TRUNCATED) | [{"id":"16316465_T1","type":"Species","text":["human"],"offsets":[[3043,3048]],"normalized":[]},{"id(...TRUNCATED) | [] | [] | [] |
17389645 | 17389645 | [{"id":"17389645__text","type":"full_text","text":["Mapping sites within the genome that are hyperse(...TRUNCATED) | [{"id":"17389645_T1","type":"Anatomy","text":["brain"],"offsets":[[8952,8957]],"normalized":[]},{"id(...TRUNCATED) | [] | [] | [] |
17381551 | 17381551 | [{"id":"17381551__text","type":"full_text","text":["This work uncovers novel mechanisms of aging wit(...TRUNCATED) | [{"id":"17381551_T1","type":"GeneProtein","text":["M-cadherin"],"offsets":[[17672,17682]],"normalize(...TRUNCATED) | [] | [] | [] |
Dataset Card for CellFinder
The CellFinder project aims to create a stem cell data repository by linking information from existing public databases and by performing text mining on the research literature. The first version of the corpus is composed of 10 full text documents containing more than 2,100 sentences, 65,000 tokens and 5,200 annotations for entities. The corpus has been annotated with six types of entities (anatomical parts, cell components, cell lines, cell types, genes/protein and species) with an overall inter-annotator agreement around 80%.
See: https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/
Citation Information
@inproceedings{neves2012annotating,
title = {Annotating and evaluating text for stem cell research},
author = {Neves, Mariana and Damaschun, Alexander and Kurtz, Andreas and Leser, Ulf},
year = 2012,
booktitle = {
Proceedings of the Third Workshop on Building and Evaluation Resources for
Biomedical Text Mining\ (BioTxtM 2012) at Language Resources and Evaluation
(LREC). Istanbul, Turkey
},
pages = {16--23},
organization = {Citeseer}
}
- Downloads last month
- 151