[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …
learning models have established their usefulness in biomedical applications, especially in …
A multimodal deep learning framework for predicting drug–drug interaction events
Abstract Motivation Drug–drug interactions (DDIs) are one of the major concerns in
pharmaceutical research. Many machine learning based methods have been proposed for …
pharmaceutical research. Many machine learning based methods have been proposed for …
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
One of the main problems with the joint use of multiple drugs is that it may cause adverse
drug interactions and side effects that damage the body. Therefore, it is important to predict …
drug interactions and side effects that damage the body. Therefore, it is important to predict …
Attention-based knowledge graph representation learning for predicting drug-drug interactions
Drug–drug interactions (DDIs) are known as the main cause of life-threatening adverse
events, and their identification is a key task in drug development. Existing computational …
events, and their identification is a key task in drug development. Existing computational …
A comprehensive review of computational methods for drug-drug interaction detection
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance,
which provides effective and safe co-prescriptions of multiple drugs. Since laboratory …
which provides effective and safe co-prescriptions of multiple drugs. Since laboratory …
Efficient prediction of drug–drug interaction using deep learning models
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 …
However, prediction of drug–drug interaction is defined as an ill‐posed problem, because …
Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs
The proposed study evaluates the efficacy of knowledge transfer gained through an
ensemble of modality-specific deep learning models toward improving the state-of-the-art in …
ensemble of modality-specific deep learning models toward improving the state-of-the-art in …
A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
Drug–drug interactions (DDIs) prediction is a challenging task in drug development and
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
Multi-relational contrastive learning graph neural network for drug-drug interaction event prediction
Drug-drug interactions (DDIs) could lead to various unexpected adverse consequences, so-
called DDI events. Predicting DDI events can reduce the potential risk of combinatorial …
called DDI events. Predicting DDI events can reduce the potential risk of combinatorial …