Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions
Motivation Due to cancer heterogeneity, the therapeutic effect may not be the same when a
cohort of patients of the same cancer type receive the same treatment. The anticancer drug …
cohort of patients of the same cancer type receive the same treatment. The anticancer drug …
Improving drug response prediction based on two-space graph convolution
Patients with the same cancer types may present different genomic features and therefore
have different drug sensitivities. Accordingly, correctly predicting patients' responses to the …
have different drug sensitivities. Accordingly, correctly predicting patients' responses to the …
Graph convolutional network for drug response prediction using gene expression data
Genomic profiles of cancer patients such as gene expression have become a major source
to predict responses to drugs in the era of personalized medicine. As large-scale drug …
to predict responses to drugs in the era of personalized medicine. As large-scale drug …
GADRP: graph convolutional networks and autoencoders for cancer drug response prediction
H Wang, C Dai, Y Wen, X Wang, W Liu… - Briefings in …, 2023 - academic.oup.com
Drug response prediction in cancer cell lines is of great significance in personalized
medicine. In this study, we propose GADRP, a cancer drug response prediction model …
medicine. In this study, we propose GADRP, a cancer drug response prediction model …
DeepCDR: a hybrid graph convolutional network for predicting cancer drug response
Motivation Accurate prediction of cancer drug response (CDR) is challenging due to the
uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have …
uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have …
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
Predicting the response of a cancer cell line to a therapeutic drug is an important topic in
modern oncology that can help personalized treatment for cancers. Although numerous …
modern oncology that can help personalized treatment for cancers. Although numerous …
DualGCN: a dual graph convolutional network model to predict cancer drug response
Background Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug
response is important to improve anti-cancer drug treatment and guide anti-cancer drug …
response is important to improve anti-cancer drug treatment and guide anti-cancer drug …
DRPreter: interpretable anticancer drug response prediction using knowledge-guided graph neural networks and transformer
Some of the recent studies on drug sensitivity prediction have applied graph neural
networks to leverage prior knowledge on the drug structure or gene network, and other …
networks to leverage prior knowledge on the drug structure or gene network, and other …
Graph convolutional networks for drug response prediction
Background: Drug response prediction is an important problem in computational
personalized medicine. Many machine-learning-based methods, especially deep learning …
personalized medicine. Many machine-learning-based methods, especially deep learning …
Predicting drug response based on multi-omics fusion and graph convolution
W Peng, T Chen, W Dai - IEEE Journal of Biomedical and …, 2021 - ieeexplore.ieee.org
Different cancer patients may respond differently to cancer treatment due to the
heterogeneity of cancer. It is an urgent task to develop an efficient computational method to …
heterogeneity of cancer. It is an urgent task to develop an efficient computational method to …