Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Emden: A novel method integrating graph and transformer representations for predicting the effect of mutations on clinical drug response.

Computers in biology and medicine - 1 Dec 2023

Liu Zhe, Bao Yihang, Wang Weidi, Pan Liangwei, Wang Han, Lin Guan Ning

Abstract excerpt

Precision medicine based on personalized genomics provides promising strategies to enhance the efficacy of molecular-targeted therapies. However, the clinical effectiveness of drugs has been severely limited due to genetic variations that lead to drug resistance. Predicting the impact of missense mutations on clinical drug response is an essential way to reduce the cost of clinical trials and understand genetic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Genomics
Share to Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Molecular Targeted Therapy

Share to Mutation

Share to Mutation, Missense

Source

Identifiers

PMID: 37976823
DOI: 10.1016/j.compbiomed.2023.107678

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Emden: A novel method integrating graph and transformer representations for predicting the effect of mutations on clinical drug response.PMID 37976823Read ↗

DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene OntologiesSemantic proximity Read ↗

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural contextSemantic proximity Read ↗

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.Semantic proximity Read ↗

A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response PredictionsSemantic proximity Read ↗

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target AffinitySemantic proximity Read ↗

Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution NetworksSemantic proximity Read ↗

DLRNA-BERTa: A transformer approach for RNA-drug binding affinity predictionSemantic proximity Read ↗

Extracting Mutant-Affected Protein-Protein Interactions via Gaussian-Enhanced Representation and Contrastive Learning.Semantic proximity Read ↗

Inferring the effects of protein variants on protein–protein interactions with an interpretable deep-learning modelSemantic proximity Read ↗

The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human GenomicsSemantic proximity Read ↗

Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target PrioritizationSemantic proximity Read ↗

BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language modelSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.