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

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

Molecular informatics - 1 Jun 2024

Dafniet Bryan, Taboureau Olivier

Abstract excerpt

Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective...

Read the complete abstract on PubMed

Topics

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

Share to Deep Learning
Share to Drug-Related Side Effects and Adverse Reactions

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Genome-Wide Association Study

Share to Neural Networks, Computer

Share to Polymorphism, Single Nucleotide

Source

Identifiers

PMID: 38850150
DOI: 10.1002/minf.202400021

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.PMID 38850150Read ↗

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.Semantic proximity Read ↗

D4: Deep Drug-drug interaction Discovery and DemystificationSemantic proximity Read ↗

Pharmacogenomics-Driven Multimodal Data Integration Improves Predictions of Adverse Drug Reactions in Cancer Patients using Machine LearningSemantic proximity Read ↗

Disentangling the contribution of disease genes to drug therapeutic and side effectsSemantic proximity Read ↗

Genomics of Adverse Drug Reactions.Semantic proximity Read ↗

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations.Semantic proximity Read ↗

DPDDI:a Deep Predictor for Drug-Drug InteractionsSemantic proximity Read ↗

Digging up the human genome: current progress in deciphering adverse drug reactions.Semantic proximity Read ↗

DeepADR: Multi-modal Prediction of Adverse Drug Reaction Frequency by Integrating Early-Stage Drug Discovery Information via Kolmogorov-Arnold NetworksSemantic proximity Read ↗

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based AnalysisSemantic proximity Read ↗

A transformer model explaining mechanisms of drug therapeutic and adverse effectsSemantic proximity Read ↗

Harvesting Candidate Genes Responsible for Serious Adverse Drug Reactions from a Chemical-Protein InteractomeSemantic proximity Read ↗

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