[HTML][HTML] A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
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 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 …
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 …
Hinge-loss markov random fields and probabilistic soft logic
A fundamental challenge in developing high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …
balancing the need to model rich, structured domains with the ability to scale to big data …
[HTML][HTML] 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 …
[HTML][HTML] To embed or not: network embedding as a paradigm in computational biology
Current technology is producing high throughput biomedical data at an ever-growing rate. A
common approach to interpreting such data is through network-based analyses. Since …
common approach to interpreting such data is through network-based analyses. Since …
Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review
Y Zhang, Z Deng, X Xu, Y Feng… - Journal of chemical …, 2023 - ACS Publications
Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …
effects on patients and can lead to serious consequences. Predicting these events …
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 …