Journal of evaluation in clinical practice
Feb 1, 2025
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 ...
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 ...
IEEE journal of biomedical and health informatics
Dec 1, 2024
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-...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
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...
Journal of the American Medical Informatics Association : JAMIA
Jun 20, 2024
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...
MOTIVATION: Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but c...
INTRODUCTION: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.
BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topol...
Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and cl...
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. Howeve...
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