Article

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

2024-08-27

Abstract excerpt

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is importan...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4d5e5be5-1261-5218-b3eb-6543f48d47eb
DOI: 10.20944/preprints202408.1941.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous NetworkDOI 10.20944/preprints202408.1941.v1Read

DeepCDR: a hybrid graph convolutional network for predicting cancer drug responseSemantic proximity Read

Looking at the BiG picture: Incorporating bipartite graphs in drug response predictionSemantic proximity Read

Integrating Protein-Protein Interaction Information into Drug Response Prediction by Graph Neural EncodingSemantic proximity Read

Deep learning and multi-omics approach to predict drug responses in cancer.Semantic proximity Read

Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural NetworkSemantic proximity Read

DruID: Personalized <b>Dru</b> g Recommendations by <b>I</b> ntegrating Multiple Biomedical <b>D</b> atabases for CancerSemantic proximity Read

HGACL-DRP: Heterogeneous Graph Attention Dual-Perturbation Contrastive Learning Network for Drug Response PredictionSemantic proximity Read

DrugSAGE: an aggregation-based method for drug response imputationSemantic proximity Read

BKDRP: A Biological Knowledge-Driven Approach for Drug Response Prediction Using Multi-Omics Data in Cancer Cell LinesSemantic proximity Read

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response PredictionSemantic proximity Read

ICAN: interpretable cross-attention network for identifying drug and target protein interactionsSemantic proximity Read

Deep learning-driven drug response prediction and mechanistic insights in cancer genomicsSemantic proximity Read