A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
Genomics & Informatics
;
: e18-2019.
Article
in English
| WPRIM
| ID: wpr-763806
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Comprehension
Language:
English
Journal:
Genomics & Informatics
Year:
2019
Type:
Article
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