Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
problem Capturing long-range dependencies in biomedical data.
method Combines logical rules with neural multi-hop reasoning using reinforcement learning.
result Our method outperforms baseline methods in drug repurposing tasks.
Paper proposes an inductive RGCN for few-shot link prediction in drug-repurposing.
problem Predicting rare interactions in drug-repurposing for novel diseases.
method Proposes an inductive RGCN to learn relation embeddings for few-shot learning.
result Significantly outperforms state-of-the-art models in few-shot learning tasks.
Machine learning identifies potential drugs for COVID-19.
problem Finding effective treatments for COVID-19.
method Trained neural network models on virus protein sequences and antiviral drugs.
result Identified potential drug candidates for treating COVID-19.
Network medicine predicts repurposable drugs for COVID-19.
problem Identifying effective drugs for SARS-CoV-2 infections quickly.
method Artificial intelligence, network diffusion, and network proximity algorithms.
result A multimodal approach combining predictions from multiple algorithms outperforms individual methods.
RobKMR improves robustness in multi-omics data analysis for osteoporosis biomarker discovery.
problem Sensitivity to adversarial outliers and lack of comprehensive multi-omics data integration.
method RobKMR, a non-linear M-estimator-based approach using robust kernel centered Gram matrix and robust score test.
result Selected biomarkers (DKK1, MTND5, FASTKD2) significantly bond with four drugs for osteoporosis.
Method learns drug-disease representations for repositioning opportunities.
problem Identifying new uses for existing drugs.
method Multi-relation unsupervised graph embedding model.
result Superior prediction performance in repositioning opportunities.
Study repurposes open data to find potential COVID-19 drugs.
problem Developing effective treatments for COVID-19.
method Deep learning network-based approach using large scientific corpus.
result Identified 41 repurposable drugs for COVID-19.
PanRep learns universal node embeddings for heterogeneous graphs.
problem Learning universal node embeddings for heterogeneous graphs.
method Graph Neural Network (GNN) model with four decoders capturing different properties.
result PanRep outperforms unsupervised and supervised methods in node classification and link prediction.
New model predicts drug effects across various cell types using causal imputation.
problem Predict drug effects across different cell types given limited data.
method Introduces a novel SCM-based model class with latent factor structure and uses Synthetic Interventions estimator.
result Method outperforms other matrix completion approaches in drug repurposing dataset.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
problem Finding novel therapies for SARS-CoV-2.
method Used ChemAI, a deep neural network trained on 220M data points, to screen and rank one billion molecules from the ZINC database.
result Identified 30,000 top-ranked compounds for further bioassays.
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes f…
Paper introduces a method to predict molecule properties from diverse data sources.
problem Limited ability to accommodate scarce or fragmented training data.
method Adaptive Invariance using invariant risk minimization to generalize beyond heterogeneous data.
result Predictor outperforms state-of-the-art transfer learning methods by significant margin.
GEFA predicts drug-target affinity using graph neural networks.
problem Accurate prediction of drug-target interactions for rapid drug repurposing.
method GEFA (Graph Early Fusion Affinity) is a novel graph-in-graph neural network with attention mechanism.
result GEFA effectively models drug-target interactions, demonstrating the effectiveness of pre-trained protein embedding and nested graph representation.
Omics-GAN uses GANs to generate synthetic multi-omics data for improved disease prediction.
problem Limited sample sizes, noise, and heterogeneity in multi-omics data reduce predictive power.
method Omics-GAN is a GAN-based framework that generates high-quality synthetic multi-omics profiles.
result Synthetic datasets consistently improved prediction accuracy compared to original omics profiles.