Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
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 …
Modeling polypharmacy side effects with graph convolutional networks
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients
with complex diseases or co-existing conditions. However, a major consequence of …
with complex diseases or co-existing conditions. However, a major consequence of …
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 …
MUFFIN: multi-scale feature fusion for drug–drug interaction prediction
Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly
cause morbidity and mortality. Thus, the identification of potential DDIs is essential for …
cause morbidity and mortality. Thus, the identification of potential DDIs is essential for …
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body,
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
DPDDI: a deep predictor for drug-drug interactions
YH Feng, SW Zhang, JY Shi - BMC bioinformatics, 2020 - Springer
Background The treatment of complex diseases by taking multiple drugs becomes
increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of …
increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of …
Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network
Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions
Drug–drug interactions are one of the major concerns of drug discovery, and the accurate
prediction of drug–drug interactions is important for drug safety surveillance. However, most …
prediction of drug–drug interactions is important for drug safety surveillance. However, most …