Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks-Reference-Cited by-同舟云学术

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Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks

Published:2024-02 Issue: Volume:222 Page:41-50
ISSN:1046-2023
Container-title:Methods
language:en
Short-container-title:Methods

Author:

Liu Hancheng,Peng Wei,Dai Wei,Lin Jiangzhen,Fu Xiaodong,Liu Li,Liu Lijun,Yu Ning

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology,Molecular Biology

Reference39 articles.

1. Deep learning for drug response prediction in cancer;Baptista;Brief. Bioinform.,2021

2. Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology;Marquart;JAMA Oncol.,2018

3. Machine learning approaches to drug response prediction: challenges and recent progress;Adam;Npj Precis. Oncol.,2020

4. Intratumoral heterogeneity in cancer progression and response to immunotherapy;Vitale;Nat. Med.,2021

5. A landscape of pharmacogenomic interactions in cancer;Iorio;Cell,2016

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1. DNA binding/cleavage study of mixed ligand complexes of di-/triorganotin 4-fluoroanthranilates with 1,10-phenanthroline/benzimidazole: Synthesis, characterization, X-ray structures of [Bu2Sn(FA)2.phen], [Me3Sn(FA).bz], and DFT calculations;Inorganica Chimica Acta;2024-11

2. Artificial intelligence and Computer-Aided Drug Discovery: Methods Development and Application;Methods;2024-09

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