Article

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

2021-01-27

Abstract excerpt

Computational models for drug sensitivity prediction have the potential to revolutionise personalized cancer medicine. Drug sensitivity assays, as well as profiling of cancer cell lines and drugs becomes increasingly available for training such models. Machine learning methods for drug sensitivity prediction must be optimized for: (i) leveraging the wealth of information about both cancer cell lines and drugs, (ii...

Topics

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

Identifiers and source

Literature Corpus work: 0961517e-2b3f-50c4-833d-add5f072cfd9
DOI: 10.1101/2021.01.26.428272

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell linesDOI 10.1101/2021.01.26.428272Read

BKDRP: A Biological Knowledge-Driven Approach for Drug Response Prediction Using Multi-Omics Data in Cancer Cell LinesSemantic proximity Read

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

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

Predicting Cancer Drug Response Using a Recommender SystemSemantic proximity Read

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

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

Discovering the mechanism of action of drugs with a sparse explainable network.Semantic proximity Read

Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning ModelsSemantic proximity Read

Explainable drug sensitivity prediction through cancer pathway enrichment.Semantic proximity Read

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivitySemantic proximity Read

A Deep Learning Framework for Prediction of Clinical Drug Response of Cancer Patients and Identification of Drug Sensitivity Biomarkers using Preclinical SamplesSemantic proximity Read

Robust Prediction of Patient-Specific Clinical Response to Unseen Drugs From in vitro Screens Using Context-aware Deconfounding AutoencoderSemantic proximity Read