A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
- Author:
Mina GACHLOO
1
;
Yuxing WANG
;
Jingbo XIA
Author Information
- Publication Type:Review
- Keywords: BioNLP; drug knowledge discovery; tensor decomposition
- MeSH: Comprehension
- From:Genomics & Informatics 2019;17(2):e18-
- CountryRepublic of Korea
- Language:English
- Abstract: Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different resources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or tensor decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
