AIMC Topic: Drug Interactions

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Machine Learning for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland) Aug 31, 2018
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, h...
Drug Interactions Databases as Topic Algorithms Drug Delivery Systems Drug Discovery Humans Machine Learning
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Drug-drug interaction extraction from biomedical texts using long short-term memory network.

Journal of biomedical informatics Aug 21, 2018
The simultaneous administration of multiple drugs increases the probability of interaction among them, as one drug may affect the activities of others. This interaction among drugs may have a positive or negative impact on the therapeutic outcomes. T...
Neural Networks, Computer Reproducibility of Results Support Vector Machine Drug Interactions Software Algorithms Language Linear Models Knowledge Bases Polypharmacy Medical Informatics Linguistics Semantics
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End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Aug 7, 2018
Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a ...
Deep Learning Drug Interactions Computational Biology Databases, Chemical Pharmaceutical Preparations
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Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Artificial intelligence in medicine Aug 6, 2018
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals h...
Risk Assessment Drug Interactions Supervised Machine Learning Adverse Drug Reaction Reporting Systems Patient Safety Drug-Related Side Effects and Adverse Reactions Humans Health Information Exchange Databases, Factual Data Mining Online Social Networking Risk Factors Pharmacovigilance
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A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
Supervised Machine Learning Drug-Related Side Effects and Adverse Reactions Probability Technology, Pharmaceutical Predictive Value of Tests Pharmaceutical Preparations Drug Interactions Drug Design Computational Biology Drug Development Software Algorithms
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A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases.

Computer methods and programs in biomedicine Jun 22, 2018
BACKGROUND AND OBJECTIVE: Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communicable diseases (NCDs), the non-transmissible and long-lasting diseases since they are the leading ca...
Neural Networks, Computer Machine Learning Algorithms Databases, Factual Computer Simulation Reproducibility of Results Humans Cytochrome P-450 Enzyme System Noncommunicable Diseases Simvastatin Quantitative Structure-Activity Relationship Drug Interactions False Positive Reactions Probability Support Vector Machine Cluster Analysis Area Under Curve
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Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

Molecular pharmaceutics May 30, 2018
Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a co...
Protein Isoforms Humans Cytochrome P-450 Enzyme Inhibitors Drug Interactions Cytochrome P-450 Enzyme System Quantitative Structure-Activity Relationship Neural Networks, Computer Machine Learning Algorithms
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Extending the DIDEO ontology to include entities from the natural product drug interaction domain of discourse.

Journal of biomedical semantics May 9, 2018
BACKGROUND: Prompted by the frequency of concomitant use of prescription drugs with natural products, and the lack of knowledge regarding the impact of pharmacokinetic-based natural product-drug interactions (PK-NPDIs), the United States National Cen...
Drug Interactions Biological Ontologies Biological Products
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What matters in a transferable neural network model for relation classification in the biomedical domain?

Artificial intelligence in medicine Apr 13, 2018
A lack of sufficient labeled data often limits the applicability of advanced machine learning algorithms to real life problems. However, the efficient use of transfer learning (TL) has been shown to be very useful across domains. TL make use of valua...
Drug Interactions Datasets as Topic Neural Networks, Computer Machine Learning Algorithms
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A hybrid model based on neural networks for biomedical relation extraction.

Journal of biomedical informatics Mar 27, 2018
Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutiona...
Natural Language Processing Medical Informatics Algorithms Humans Research Design Pattern Recognition, Automated Drug Interactions Neural Networks, Computer False Positive Reactions Data Mining Models, Statistical Machine Learning
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