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 …
Graph convolutional networks for computational drug development and discovery
Despite the fact that deep learning has achieved remarkable success in various domains
over the past decade, its application in molecular informatics and drug discovery is still …
over the past decade, its application in molecular informatics and drug discovery is still …
A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …
areas, such as virtual screening, drug repurposing and identification of potential drug side …
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 …
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 …
Deep learning improves prediction of drug–drug and drug–food interactions
Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
[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 …
Harnessing biomedical literature to calibrate clinicians' trust in AI decision support systems
Clinical decision support tools (DSTs), powered by Artificial Intelligence (AI), promise to
improve clinicians' diagnostic and treatment decision-making. However, no AI model is …
improve clinicians' diagnostic and treatment decision-making. However, no AI model is …
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 …