Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey
M Chen, B Zhang, Z Cai, S Seery, MJ Gonzalez… - Frontiers in …, 2022 - frontiersin.org
Background Artificial intelligence (AI) needs to be accepted and understood by physicians
and medical students, but few have systematically assessed their attitudes. We investigated …
and medical students, but few have systematically assessed their attitudes. We investigated …
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 …
Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction
Drug-drug interaction (DDI) plays an important role in drug development and administration.
Identifying potential DDI effectively is critical for public health since it can avoid adverse drug …
Identifying potential DDI effectively is critical for public health since it can avoid adverse drug …
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2
The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of
deaths since 2019. Although a variety of computational methods have been proposed to …
deaths since 2019. Although a variety of computational methods have been proposed to …
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Drug repositioning (DR) is a promising strategy to discover new indicators of approved
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
A computational approach to drug repurposing using graph neural networks
S Doshi, SP Chepuri - Computers in Biology and Medicine, 2022 - Elsevier
Drug repurposing is an approach to identify new medical indications of approved drugs. This
work presents a graph neural network drug repurposing model, which we refer to as …
work presents a graph neural network drug repurposing model, which we refer to as …
Predicting protein-protein interactions using sequence and network information via variational graph autoencoder
Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of
computational algorithms have been developed to predict PPIs. Though effective, their …
computational algorithms have been developed to predict PPIs. Though effective, their …
Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
Background The pharmaceutical field faces a significant challenge in validating drug target
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2
Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality
rate with the effect of vaccine and immunity. The hospitalization and intense demand …
rate with the effect of vaccine and immunity. The hospitalization and intense demand …
Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques
Drug discovery relies on predicting drug-target interaction (DTI), which is an important
challenging task. The purpose of DTI is to identify the interaction between drug chemical …
challenging task. The purpose of DTI is to identify the interaction between drug chemical …