Article

Domain-adversarial learning predicts clinically actionable drug combination synergy in leukemia patients using bulk transcriptomics data

2026-05-20

Abstract excerpt

Deep learning has gained popularity in drug combination synergy prediction; however, DL models require large training datasets from cell line pharmacogenomic screens that poorly capture the heterogeneity in transcriptomic features and phenotypic responses seen in patients. To that end, we developed a domain-adversarial neural network (DANN) for personalized drug synergy prediction that accounts for systematic diff...

Topics

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

Identifiers and source

Literature Corpus work: 6aac3f6d-5c28-5882-b0d8-bf6a54a1d2e7
DOI: 10.64898/2026.05.18.725869

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Domain-adversarial learning predicts clinically actionable drug combination synergy in leukemia patients using bulk transcriptomics dataDOI 10.64898/2026.05.18.725869Read

PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in CancerSemantic proximity Read

From Cell-Lines to Cancer Patients: Personalized Drug Synergy PredictionSemantic proximity Read

Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep LearningSemantic proximity Read

MatchMaker: A Deep Learning Framework for Drug Synergy PredictionSemantic proximity Read

Pisces: A multi-modal data augmentation approach for drug combination synergy predictionSemantic proximity Read

Dose-dependent modeling of combinatorial drug responses stratifies patient survival and reveals therapeutic vulnerabilities in precision oncologySemantic proximity Read

SynVerse: A Framework for Systematic Evaluation of Deep Learning Based Drug Synergy Prediction ModelsSemantic proximity Read

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

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.Semantic proximity Read

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination predictionSemantic proximity Read

Condition-matched in silico prediction of drug transcriptional responses enables mechanism-guided screening and combination discoverySemantic proximity Read

UniCure: A Foundation Model for Predicting Personalized Cancer Therapy ResponseSemantic proximity Read