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KMID : 1132720190170020018
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Genomics & Informatics
2019 Volume.17 No. 2 p.18 ~ p.18
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A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
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Gachloo Mina
Wang Yuxing
Xia Jingbo
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Abstract
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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 sources 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 matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
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KEYWORD
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BioNLP, drug knowledge discovery, tensor decomposition
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