[HTML][HTML] Advances and trends in omics technology development
X Dai, L Shen - Frontiers in Medicine, 2022 - frontiersin.org
The human history has witnessed the rapid development of technologies such as high-
throughput sequencing and mass spectrometry that led to the concept of 'omics' and …
throughput sequencing and mass spectrometry that led to the concept of 'omics' and …
[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 …
Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism
T Wang, J Sun, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention
R Meng, S Yin, J Sun, H Hu, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
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 …
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 …
Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
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 …
Attention is all you need: utilizing attention in AI-enabled drug discovery
Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …
development due to their outstanding performance and interpretability in handling complex …
MCFF-MTDDI: multi-channel feature fusion for multi-typed drug–drug interaction prediction
Adverse drug–drug interactions (DDIs) have become an increasingly serious problem in the
medical and health system. Recently, the effective application of deep learning and …
medical and health system. Recently, the effective application of deep learning and …