{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang K"],"funding":["NHLBI NIH HHS","NIMH NIH HHS","Biotechnology and Biological Sciences Research Council","Engineering and Physical Sciences Research Council"],"pagination":["38"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8528865"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(1)"],"pubmed_abstract":["Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades<sup>1,2</sup>, the most dramatic advances in MR have followed in the wake of critical corpus development<sup>3</sup>. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet<sup>4</sup> was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientifi"],"journal":["NPJ systems biology and applications"],"pubmed_title":["NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding."],"pmcid":["PMC8528865"],"funding_grant_id":["EP/S014128/1","EP/R022925/2","EP/R022925/1","GR/S47656/01","EP/K030469/1","BB/F008228/1","EP/R022941/1","U01 HL108634","BEP17028","BB/G000662/1","EP/K030582/1","BB/D006503/1","R01 HL122712","BB/E018025/1","EP/M015688/1","P50 MH094267","EP/W004801/1","1441239","K12 HL143959","BB/D00425X/1","EP/M015661/1"],"pubmed_authors":["Alachram H","Christopoulou F","Matthew J","Stevens R","Chambers B","Schoene AM","Wang K","Sheng E","Ananiadou S","Hermjakob U","Galstyan A","Garg S","Rzhetsky A","Evans JA","King R","Pan W","Soldatova L","Li M","Beißbarth T","Marcu D","Li Y","Wingender E","Gao X","Ambite JL","Khomtchouk BB"],"additional_accession":[]},"is_claimable":false,"name":"NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.","description":"Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades<sup>1,2</sup>, the most dramatic advances in MR have followed in the wake of critical corpus development<sup>3</sup>. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet<sup>4</sup> was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientifi","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-05-07T22:20:39.799Z","creation":"2025-02-19T03:53:08.14Z"},"accession":"S-EPMC8528865","cross_references":{"pubmed":["34671039"],"doi":["10.1038/s41540-021-00200-x"]}}