Novel deep learning model for more accurate prediction of drug-drug interaction effects

Background Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 20; no. 1; pp. 415 - 8
Main Authors: Lee, Geonhee, Park, Chihyun, Ahn, Jaegyoon
Format: Journal Article
Language:English
Published: London BioMed Central 06.08.2019
BioMed Central Ltd
BMC
Subjects:
Algorithms
Autoencoder
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Deep learning
Drug interactions
Drug-drug interaction
Genes
Legal fees
Life Sciences
Machine Learning and Artificial Intelligence in Bioinformatics
Mathematical models
Methodology
Methodology Article
Microarrays
Novels
Online health care information services
Pharmaceutical industry
Similarity profile
South Korea
Deep learning
Similarity profile
Drug-drug interaction
Autoencoder
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance. Results In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The results show that GSP and TSP increase the prediction accuracy when using SSP alone, and the autoencoder is more effective than PCA for reducing the dimensions of each profile. Our model showed better performance than the existing methods, and identified a number of novel DDIs that are supported by medical databases or existing research. Conclusions We present a novel deep learning model for more accurate prediction of DDIs and their effects, which may assist in future research to discover novel DDIs and their pharmacological effects.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3013-0