Genomics Inform.  2019 Jun;17(2):e18. 10.5808/GI.2019.17.2.e18.

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

• Gachloo M
• Wang Y
• Xia J

• KMID: 2462028
• DOI: http://doi.org/10.5808/GI.2019.17.2.e18

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.

Keyword

BioNLP; drug knowledge discovery; tensor decomposition

MeSH Terms

Comprehension