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Article . 2021
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DrugProt Large-Scale Text Mining corpus: Biocreative VII Track 1 - Text mining drug and chemical-protein interactions

Authors: Miranda-Escalada, Antonio; Jouni Luoma; Farrokh Mehryary; Sampo Pyysalo; Krallinger, Martin;

DrugProt Large-Scale Text Mining corpus: Biocreative VII Track 1 - Text mining drug and chemical-protein interactions

Abstract

This Zenodo contains the BioCreative VII Large scale DrugProt Additional Subtrack abstracts and entity annotations. Please cite if you use any DrugProt resource: Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080 @article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} } Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." Proceedings of the seventh BioCreative challenge evaluation workshop. 2021. @inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} } Abstracts large_scale_abstracts.tsv This file contains plain-­text, UTF8-­encoded, NFC normalized DrugProt PubMed records in a tab­ ‐ separated format. In total 2366081 records are provided, where each line in the fails contains a single PMID, title and abstract separated by tabulators. Due to PubMed inconsistencies, there is a minor percentage of duplicated records. Indeed, we have identified 222 records with different PMID but the same abstract title and body. Entity mention annotations large_scale_entities.tsv. This file contains the automatically labeled mention annotations of chemical compounds and genes/proteins (so-­called gene and protein-related objects as defined during BioCreative V) generated for the Large Scale records. There are 53993602 entity annotations. Related resources: Web DrugProt corpus Evaluation library Online evaluation (CodaLab) Relation annotation guidelines Gene and protein annotation guidelines Chemicals and drugs annotation guidelines DrugProt Silver Standard Knowledge Graph FAQ DrugProt Large Scale Additional SubTrack DrugProt Large Scale document collection protocol DrugProt Complete PubMed Knowledge Graph

Keywords

biocreative, relation extraction, NER, biomedical NLP, NLP

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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