Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network
The determination of transcriptome profiles that mediate immune therapy in cancer remains
a major clinical and biological challenge. Despite responses induced by immune-check …
a major clinical and biological challenge. Despite responses induced by immune-check …
Network-based machine learning approach to predict immunotherapy response in cancer patients
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer
patients over the past several years. However, only a minority of patients respond to ICI …
patients over the past several years. However, only a minority of patients respond to ICI …
Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only
some patients respond to ICIs, and current biomarkers for ICI efficacy have limited …
some patients respond to ICIs, and current biomarkers for ICI efficacy have limited …
Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks
Q Wu, J Wang, Z Sun, L Xiao, W Ying… - IEEE journal of …, 2023 - ieeexplore.ieee.org
Immunotherapy is an effective way to treat non-small cell lung cancer (NSCLC). The efficacy
of immunotherapy differs from person to person and may cause side effects, making it …
of immunotherapy differs from person to person and may cause side effects, making it …
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 …
Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy
Immunotherapy has shown significant promise as a treatment for cancer, such as lung
cancer and melanoma. However, only 10%–30% of the patients respond to treatment with …
cancer and melanoma. However, only 10%–30% of the patients respond to treatment with …
Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma
X He, Y Su, P Liu, C Chen, C Chen, H Guan… - Journal of Cancer …, 2023 - Springer
Purpose Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate.
Immunotherapy has become a breakthrough in cancer treatment and improves patient …
Immunotherapy has become a breakthrough in cancer treatment and improves patient …
A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features
B Ye, Z Li, Q Wang - Frontiers in Immunology, 2024 - frontiersin.org
Background Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer
treatment, yet the absence of reliable biomarkers hampers precise patient response …
treatment, yet the absence of reliable biomarkers hampers precise patient response …
PMSG-Net: A priori-guided multilevel graph transformer fusion network for immunotherapy efficacy prediction
W Yang, W Wu, L Wang, S Zhang, J Zhao… - Computers in Biology and …, 2023 - Elsevier
In the case of specific immunotherapy regimens and access to pre-treatment CT scans,
developing reliable, interpretable intelligent image biomarkers to predict efficacy is essential …
developing reliable, interpretable intelligent image biomarkers to predict efficacy is essential …
Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors
The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients,
making it challenging to pre-determine whether a particular patient will respond to …
making it challenging to pre-determine whether a particular patient will respond to …