AIMC Topic: Drug Interactions

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Extracting drug-drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network.

Journal of biomedical informatics Sep 27, 2019
Drug-drug interactions are critical in studying drug side effects. Thus, quickly and accurately identifying the relationship between drugs is necessary. Current methods for biomedical relation extraction include only the sequential information of sen...
Drug Interactions Data Mining Medical Informatics Neural Networks, Computer Natural Language Processing Humans
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A two-stage deep learning approach for extracting entities and relationships from medical texts.

Journal of biomedical informatics Sep 20, 2019
This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many natural language understanding applications in the biomedical domain. Autom...
Drug Interactions Data Mining Clinical Coding Electronic Health Records Deep Learning Humans
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Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BMC bioinformatics Aug 6, 2019
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 ...
Models, Theoretical Neural Networks, Computer Support Vector Machine Humans Area Under Curve Drug Interactions Databases, Factual Deep Learning
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Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures.

PloS one Aug 1, 2019
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction...
Neural Networks, Computer Algorithms Humans Computer Graphics Computational Biology Databases, Pharmaceutical Drug Interactions Retrospective Studies Pattern Recognition, Automated ROC Curve
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Artificial Intelligence in Drug Treatment.

Annual review of pharmacology and toxicology Jul 26, 2019
The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This...
Humans Models, Theoretical Pharmaceutical Preparations Drug Interactions Artificial Intelligence Precision Medicine Drug Administration Schedule
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Exploring semi-supervised variational autoencoders for biomedical relation extraction.

Methods (San Diego, Calif.) Feb 27, 2019
The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical...
Supervised Machine Learning Data Mining Protein Interaction Maps Drug Interactions Algorithms Biomedical Research Neural Networks, Computer
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BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies.

BMC bioinformatics Jan 7, 2019
BACKGROUND: Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. I...
Biological Ontologies Gene Ontology Natural Language Processing Drug Interactions Data Mining Databases, Factual Deep Learning Neural Networks, Computer Software Humans
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Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies.

Journal of clinical pharmacy and therapeutics Dec 18, 2018
WHAT IS KNOWN AND OBJECTIVE: Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the devel...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Reproducibility of Results Support Vector Machine Humans Databases, Factual Drug Interactions Machine Learning Computer Simulation
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Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.

Human heredity Oct 22, 2018
AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs...
Drug-Related Side Effects and Adverse Reactions Machine Learning Genome-Wide Association Study Humans Drug Interactions Neural Networks, Computer
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Deep learning-based transcriptome data classification for drug-target interaction prediction.

BMC genomics Sep 24, 2018
BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded...
Gene Expression Profiling Humans Transcriptome Algorithms Computer Simulation Models, Theoretical Proteins Databases, Factual Molecular Targeted Therapy Drug Discovery Machine Learning Drug Interactions
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