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Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
We propose a novel neural method to extract drug-drug interactions (DDIs...
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GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domai...
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A critical assessment of conformal prediction methods applied in binary classification settings
In recent years there has been an increase in the number of scientific p...
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AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Deep convolutional neural networks comprise a subclass of deep neural ne...
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Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Drug similarity has been studied to support downstream clinical tasks su...
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Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
Drug-drug interactions (DDIs) are a major cause of preventable hospitali...
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A Minimal-Input Multilayer Perceptron for Predicting Drug-Drug Interactions Without Knowledge of Drug Structure
The necessity of predictive models in the drug discovery industry cannot...
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Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction
We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph CNN with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery.
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