Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data.
Keywords: Bioinformatics | Ontologies | Non-coding RNA | Biomedical Ontology | Ontology Development | Data Annotation |
@article{huang2016development,
title={{The development of non-coding RNA ontology}},
author={Huang, Jingshan and Eilbeck, Karen and Smith, Barry and Blake, Judith A and Dou, Dejing and Huang, Weili and Natale, Darren A and Ruttenberg, Alan and Huan, Jun and Zimmermann, Michael T and Jiang,Guoqian and Lin, Yu and Wu, Bin and Strachan, Harrison J. and de Silva,Nisansa and others},
journal={International journal of data mining and bioinformatics},
volume={15},
number={3},
pages={214--232},
year={2016},
publisher={Inderscience Publishers (IEL)},
abstract={Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data.},
misc={https://goo.gl/giDAfL,BIO:ONT,https://i1.rgstatic.net/publication/305349494_The_development_of_non-coding_RNA_ontology/links/5916089a4585152e199f6af9/largepreview.png},
doi={10.1504/IJDMB.2016.077072}
}
ClosedPI: Dejing Dou
We propose to advance biomedical natural language processing by combining ontology-based reasoning, semantic search, and embedding-driven methods to improve information extraction, representation, and consistency detection in biomedical texts.
ActivePI: Nisansa de Silva
The Knowledge Representation project investigates methods for structuring, aligning, and reasoning over knowledge extracted from language, with particular emphasis on low-resource settings, domain-specific semantics, and intelligent information access.