DPDDI: a deep predictor for drug-drug interactions

Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is h...

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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. 1; S. 1 - 15
Hauptverfasser:	Feng, Yue-Hua, Zhang, Shao-Wu, Shi, Jian-Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 24.09.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Agglomeration Algorithms Analysis Artificial neural networks Bioinformatics Biological properties Biomedical and Life Sciences Case studies Combination drug therapy Complications and side effects Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Convolution Datasets DDI prediction Deep neural network Drug interactions Drug-drug interaction Drugs Feature extraction Graph convolution network (GCN) Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Methodology Methodology Article Methods Microarrays Neural networks Performance prediction Propagation Side effects Toxicity Feature extraction Deep neural network DDI prediction Graph convolution network (GCN) Drug-drug interaction
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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