Genomics & Informatics

  • 제17권2호
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  • Pages.18.1-18.10
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  • 2019
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  • 1598-866X(pISSN)
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  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
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A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

  • Gachloo, Mina (Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University) ;
  • Wang, Yuxing (Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University) ;
  • Xia, Jingbo (Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University)
  • 투고 : 2019.02.23
  • 심사 : 2019.05.30
  • 발행 : 2019.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

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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참고문헌

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