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

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,...

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Main Authors: Mina Gachloo, Yuxing Wang, Jingbo Xia
Format: Article
Language:English
Published: BioMed Central 2019-06-01
Series:Genomics & Informatics
Subjects:
Online Access:http://genominfo.org/upload/pdf/gi-2019-17-2-e18.pdf
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author Mina Gachloo
Yuxing Wang
Jingbo Xia
author_facet Mina Gachloo
Yuxing Wang
Jingbo Xia
author_sort Mina Gachloo
collection DOAJ
description 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.
format Article
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institution Kabale University
issn 2234-0742
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publishDate 2019-06-01
publisher BioMed Central
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series Genomics & Informatics
spelling doaj-art-1bf643d809ff481593c799a6201e5df02025-02-02T22:28:50ZengBioMed CentralGenomics & Informatics2234-07422019-06-0117210.5808/GI.2019.17.2.e18563A review of drug knowledge discovery using BioNLP and tensor or matrix decompositionMina Gachloo0Yuxing Wang1Jingbo Xia2 Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaPrediction 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.http://genominfo.org/upload/pdf/gi-2019-17-2-e18.pdfBioNLPdrug knowledge discoverytensor decomposition
spellingShingle Mina Gachloo
Yuxing Wang
Jingbo Xia
A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
Genomics & Informatics
BioNLP
drug knowledge discovery
tensor decomposition
title A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
title_full A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
title_fullStr A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
title_full_unstemmed A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
title_short A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
title_sort review of drug knowledge discovery using bionlp and tensor or matrix decomposition
topic BioNLP
drug knowledge discovery
tensor decomposition
url http://genominfo.org/upload/pdf/gi-2019-17-2-e18.pdf
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