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Drug Interactions

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Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.

Frontiers in bioscience (Landmark edition)
: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments ...

Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings.

Briefings in bioinformatics
An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, seve...

A span-graph neural model for overlapping entity relation extraction in biomedical texts.

Bioinformatics (Oxford, England)
MOTIVATION: Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error pr...

Application of deep learning methods in biological networks.

Briefings in bioinformatics
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological system...

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Briefings in bioinformatics
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus ...

Biological applications of knowledge graph embedding models.

Briefings in bioinformatics
Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and ...

[Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse ef...

The Next Generation of Machine Learning in DDIs Prediction.

Current pharmaceutical design
Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and phar...

Deep learning for drug-drug interaction extraction from the literature: a review.

Briefings in bioinformatics
Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been wi...

Efficient prediction of drug-drug interaction using deep learning models.

IET systems biology
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting th...