Article

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

2026-04-21

Abstract excerpt

A substantial proportion of cancer patients fail to benefit from their prescribed combination regimens, yet identifying superior alternatives from the vast pharmacological space prior to treatment failure remains an unsolved clinical challenge. Existing computational approaches either rely on multi-omics profiles unavailable in standard oncological practice or reduce drug efficacy to scalar metrics that discard th...

Topics

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

Identifiers and source

Literature Corpus work: 8a2f0e1c-8d7f-580a-ba9c-6e468f7da82e
DOI: 10.64898/2026.04.16.718332

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Dose-dependent modeling of combinatorial drug responses stratifies patient survival and reveals therapeutic vulnerabilities in precision oncologyDOI 10.64898/2026.04.16.718332Read

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

Uncertainty-Aware Deep Learning for Multi-Metric and Dose-Specific Prediction of Drug SynergySemantic proximity Read

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

A precision oncology-focused deep learning framework for personalized selection of cancer therapySemantic proximity Read

Computational Advancements in Cancer Combination Therapy PredictionSemantic proximity Read

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

Explainable machine learning prediction of synergistic drug combinations for precision cancer medicineSemantic proximity Read

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

Deep characterization of cancer drugs mechanism of action by integrating large-scale genetic and drug screensSemantic proximity Read

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

BioXNet: a biologically inspired neural network for deciphering anti-cancer drug response in precision medicineSemantic proximity Read

Mechanism-aware inference of response to targeted cancer therapiesSemantic proximity Read