Article

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

2021-07-07

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug sensitivity are two major goals of individualized medicine. In this study, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines, to predict the response of cancer patients to different treatments. TINDL utilizes a...

Topics

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

Identifiers and source

Literature Corpus work: 819aeb96-b09a-521b-a1f8-8079d2bb7e18
DOI: 10.1101/2021.07.06.451273

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Deep Learning Framework for Prediction of Clinical Drug Response of Cancer Patients and Identification of Drug Sensitivity Biomarkers using Preclinical SamplesDOI 10.1101/2021.07.06.451273Read

Cancer drug sensitivity prediction from routine histology imagesSemantic proximity Read

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

Deep learning-driven drug response prediction and mechanistic insights in cancer genomicsSemantic proximity Read

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

Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell LinesSemantic proximity Read

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

Dr.Paso: Drug response prediction and analysis system for oncology researchSemantic proximity Read

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell LinesSemantic proximity Read

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

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

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