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Drug Interactions

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Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.

PloS one
Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of thes...

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Journal of biomedical informatics
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information ...

Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach.

Journal of biomedical informatics
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unst...

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. ...

Modeling analgesic drug interactions using support vector regression: a new approach to isobolographic analysis.

Journal of pharmacological and toxicological methods
BACKGROUND: Modeling drug interactions is important for illustrating combined drug actions and for predicting the pharmacological and/or toxicological effects that can be obtained using combined drug therapy.

Development and Content Analysis Protocol for Evaluating Artificial Intelligence in Drug-Related Information.

Journal of evaluation in clinical practice
INTRODUCTION: Artificial intelligence (AI) has significant transformative potential across various sectors, particularly in health care. This study aims to develop a protocol for the content analysis of a method designed to assess AI applications in ...

A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Briefings in functional genomics
Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects ...

GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...

Drug-Target Interaction Prediction via Deep Multimodal Graph and Structural Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...

Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products...