Interpretable dynamic directed graph convolutional network for multi-relational prediction of missense mutation and drug response
Q Gao, T Xu, X Li, W Gao, H Shi… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Tumor heterogeneity presents a significant challenge in predicting drug responses,
especially as missense mutations within the same gene can lead to varied outcomes such …
especially as missense mutations within the same gene can lead to varied outcomes such …
[HTML][HTML] Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation
M Cook, B Qorri, A Baskar, J Ziauddin… - Exploration of …, 2023 - explorationpub.com
Aim: Many small datasets of significant value exist in the medical space that are being
underutilized. Due to the heterogeneity of complex disorders found in oncology, systems …
underutilized. Due to the heterogeneity of complex disorders found in oncology, systems …
Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction
X Li, J Gui, Q Gao, H Shi, Z Yue - arXiv preprint arXiv:2408.17129, 2024 - arxiv.org
Graph Neural Networks have been widely applied in critical decision-making areas that
demand interpretable predictions, leading to the flourishing development of interpretability …
demand interpretable predictions, leading to the flourishing development of interpretability …