Article

ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer

2025-07-10

Abstract excerpt

Pancreatic cancer (PC) patient-derived organoids (PDOs) faithfully recapitulate therapeutic responses but face clinical translation barriers, including high costs and technical complexity. To address these problems and the lack of frameworks for PDO-based drug-response assays, we developed ODFormer, a computational framework that simulates PC PDOs to predict clinically actionable, patient-specific drug responses b...

Topics

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

Identifiers and source

Literature Corpus work: e690f0fd-0295-5b76-80a2-2e2b7d64c3ed
DOI: 10.1101/2025.07.08.663664

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic CancerDOI 10.1101/2025.07.08.663664Read

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

SCOPE: Integrating Organoid Screening and Clinical Variables Through Machine Learning for Cancer Trial Outcome PredictionSemantic proximity Read

Organoids for Functional Precision Medicine in Advanced Pancreatic Cancer.Semantic proximity Read

DELPHAI, AI Agent for Predicting Drug Response and ResistanceSemantic proximity Read

Pancreatic cancer patient-derived organoids capture therapy response and tumor evolutionSemantic proximity Read

AetherCell: A generative engine for virtual cell perturbation and <i>in vivo</i> drug discoverySemantic proximity Read

Deep Learning Paradigm for Precision Lung Cancer Therapy with AI-Driven Genotype-Phenotype Mining and Patient-Derived Organoid ValidationSemantic proximity Read

A network model for patient-derived drug response in breast cancer integrating multi-omics datasetsSemantic proximity Read

Combining molecular characteristics and therapeutic analysis of PDOs predict clinical responses and guide PDAC personalized treatmentSemantic proximity Read

Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep LearningSemantic proximity Read

Phenotypic inference from sparse tumor genomes informs an explainable deep-learning model for cancer prognosisSemantic proximity Read

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck CancerSemantic proximity Read