RegEl Database: text-mined regulatory elements from the literature and their associations to genes and disease

<pre>@article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } </pre> # RegEl PubMed Database This database contains the annotations generated by running [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) models trained on the [RegEl corpus](https://zenodo.org/record/5776679) over &gt;20M PubMed abstracts. By pairing these annotations with the one provided by PubTator this generates a large text mining database of regulatory elements associated with genes (normalized to NCBI Gene ids) and disease (normalized to either MeSH or OMIM). The tables composing the database are: * abstracts.db:<br> - pmid = PubMed ID of the given abstracts<br> - sid = sentence ID of the given abstracts (from 0 to # of sentences)<br> - text = text of the given sentence * gene.db and disease.db:<br> - pmid = PubMed ID of the given abstracts<br> - sid = sentence ID of the given abstracts (from 0 to # of sentences)<br> - etype = entity type (enhancer, promoter, TFBS)<br> - ann_text = mention of the regulatory element as found in the abstract<br> - start = position (# character) in which the mention begins<br> - end = position (# characters) in which the mention ends<br> - score = model's confidence<br> - cui = gene or disease identifier<br> - cui_symbol = official symbol of cui (if available)