AIMC Topic: Drug Interactions

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GCN-BMP: Investigating graph representation learning for DDI prediction task.

Methods (San Diego, Calif.) Jul 3, 2020
One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict...
Humans Drug Interactions Datasets as Topic Pharmacology, Clinical Forecasting Deep Learning Computational Biology
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Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Methods (San Diego, Calif.) Jun 1, 2020
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extra...
Datasets as Topic Drug Interactions Humans Pharmacology, Clinical Drug Therapy, Combination Forecasting Deep Learning Computational Biology
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Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions.

Journal of biomedical informatics May 23, 2020
Drug-drug interactions (DDIs) extraction is one of the important tasks in the field of biomedical relation extraction, which plays an important role in the field of pharmacovigilance. Previous neural network based models have achieved good performanc...
Semantics Pharmaceutical Preparations Neural Networks, Computer Drug Interactions Attention
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Drug-drug interaction extraction via hybrid neural networks on biomedical literature.

Journal of biomedical informatics Apr 23, 2020
Adverse events caused by drug-drug interaction (DDI) not only pose a serious threat to health, but also increase additional medical care expenditure. However, despite the emergence of many excellent text mining-based DDI classification methods, achie...
Neural Networks, Computer Algorithms Pharmaceutical Preparations Drug Interactions Data Mining
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BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

PLoS computational biology Apr 23, 2020
A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein inter...
Electronic Health Records Deep Learning Computational Biology Humans Algorithms PubMed Data Mining Semantics Databases, Protein Publications Drug Interactions Protein Interaction Mapping
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Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of derivi...
Polypharmacy Data Visualization Humans Neural Networks, Computer Computational Biology Algorithms Drug-Related Side Effects and Adverse Reactions Models, Biological Pharmacovigilance Drug Interactions Semantics
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Prediction of Side Effects Using Comprehensive Similarity Measures.

BioMed research international Feb 27, 2020
Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method th...
Vorinostat Clonidine Dasatinib Drug-Related Side Effects and Adverse Reactions Sitagliptin Phosphate Humans Machine Learning Polymorphism, Single Nucleotide Drug Repositioning Area Under Curve Drug Interactions
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Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.

BMC bioinformatics Dec 27, 2019
BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interacti...
Humans Machine Learning Drug Repositioning Algorithms Area Under Curve Drug Discovery Drug Interactions
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Computational prediction of cytochrome P450 inhibition and induction.

Drug metabolism and pharmacokinetics Dec 20, 2019
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is high...
Drug Interactions Cytochrome P-450 Enzyme System Cytochrome P-450 Enzyme Inhibitors Humans Machine Learning Models, Molecular
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Pharmacokinetic-pharmacodynamic assessment of the ivermectin and abamectin nematodicidal interaction in cattle.

Veterinary parasitology Dec 10, 2019
In a context of nematodicidal resistance, anthelmintic combinations have emerged as a reliable pharmacological strategy to control gastrointestinal nematodes in grazing systems of livestock production. The current work evaluated the potential drug-dr...
Rhabditida Ivermectin Cattle Animals Rhabditida Infections Cattle Diseases Random Allocation Male Antinematodal Agents Drug Interactions Haemonchiasis Haemonchus
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