On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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14 results for “drug-repurposing”

Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposingcs.LG

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.

drug repurposing neural multi-hop reasoning reinforcement learning biomedical data logical rules

2020-07-10abs ↗pdf ↗

Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingcs.LG

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.

graph neural networks few-shot learning drug-repurposing knowledge graph covid-19

2020-07-20abs ↗pdf ↗

Machine-Learning Driven Drug Repurposing for COVID-19cs.LG

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.

machine learning drug repurposing COVID-19 neural networks antiviral drugs

2020-06-25abs ↗pdf ↗

Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapiescs.CL

System automates identification of cancer drug repurposing from PubMed.

problem Manual extraction of cancer drug repurposing evidence from scientific publications is infeasible.

method NLP pipeline including querying, filtering, entity extraction, classification, and study type classification.

result Automated system extracts cancer drug repurposing evidence from PubMed abstracts.

drug repurposing cancer treatment natural language processing PubMed generic drugs

2019-11-18abs ↗pdf ↗

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19q-bio.MN

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.

drug repurposing network medicine COVID-19 repurposable drugs SARS-CoV-2

2020-04-15abs ↗pdf ↗

A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discoverystat.ML

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.

robust kernel machine regression multi-omics data osteoporosis biomarker drug repurposing adversarial outliers

2022-01-13abs ↗pdf ↗

Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embeddingcs.LG

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.

drug repositioning drug repurposing unsupervised graph embedding pharmacological effects drug-disease representations

2020-04-30abs ↗pdf ↗

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learningq-bio.QM

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.

COVID-19 drug repurposing deep learning SARS-CoV-2 transcriptomic

2020-05-21abs ↗pdf ↗

PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphscs.LG

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.

Graph Neural Networks Universal Node Embeddings Heterogeneous Graphs Node Classification Link Prediction

2020-07-20abs ↗pdf ↗

Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Modelsstat.ML

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.

Causal Imputation Counterfactual SCMs Matrix Completion Latent Factor Models Drug Repurposing

2024-02-22abs ↗pdf ↗

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networksq-bio.BM

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.

SARS-CoV-2 inhibitors deep learning chemAI virtual screening

2020-03-25abs ↗pdf ↗

Adaptive Invariance for Molecule Property Predictionq-bio.QM

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.

molecule property prediction invariant risk minimization adaptive invariance COVID-19 antivirals fragmented data

2020-05-05abs ↗pdf ↗

GEFA: Early Fusion Approach in Drug-Target Affinity Predictioncs.LG

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.

drug-target affinity graph neural networks attention mechanism pre-trained protein embedding nested graph

2020-09-25abs ↗pdf ↗

Transforming Multi-Omics Integration with GANs: Applications in Alzheimer's and Cancerq-bio.QM

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.

GANs multi-omics disease prediction Alzheimer's disease cancer

2025-10-22abs ↗pdf ↗