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

Journal: BMC bioinformatics

Published Date: Aug 6, 2019

Abstract

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.

Authors

Geonhee Lee

Department of Computer Science and Engineering, Incheon National University, Incheon, 22012, South Korea.
Chihyun Park

Dept. of Computer Science, Yonsei University, Seodaemun-gu, Seoul, Korea.
Jaegyoon Ahn

Department of Integrative Biology and Physiology, University of California, Los Angeles, USA. Electronic address: jgahn@ucla.edu.

Keywords

Area Under Curve Databases, Factual Deep Learning Drug Interactions Humans Models, Theoretical Neural Networks, Computer Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31387547)

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

Abstract

Authors

Keywords

External Resources

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