Article

A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes

2023-11-17

Abstract excerpt

Next generation sequencing (NGS) of tumours is increasingly utilised in oncological practice, however only a minority of patients harbour oncogenic driver mutations benefiting from targeted therapy. Development of a drug response prediction (DRP) model based on available genomic data is important for the ‘untargetable’ majority of cases. Prior DRP models typically rely on whole transcriptome and whole exome sequen...

Topics

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

Identifiers and source

Literature Corpus work: 70f88ca8-c34e-5ead-9904-40f2e9fa1117
DOI: 10.1101/2023.11.17.23298665

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genesDOI 10.1101/2023.11.17.23298665Read

PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumorsSemantic proximity Read

TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from <i>in vitro</i> to <i>in vivo</i> settingsSemantic proximity Read

TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusionSemantic proximity Read

Predicting single-cell drug sensitivity utilizing adaptive weighted features for deep transfer learningSemantic proximity Read

SCREP: Towards Single-Cell Drug Response Prediction by Pharmacogenomic Embedding Enhanced Meta-Pretraining and Few-Shot Transfer LearningSemantic proximity Read

Deep Transfer Learning of Drug Responses by Integrating Bulk and Single-cell RNA-seq dataSemantic proximity Read

A Multi-Modal Genomic Knowledge Distillation Framework for Drug Response PredictionSemantic proximity Read

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancerSemantic proximity Read

Patient Informed Domain Adaptation Improves Clinical Drug Response PredictionSemantic proximity Read

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer studySemantic proximity Read

Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.Semantic proximity Read

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response PredictionSemantic proximity Read