Presentation date: June 13, 2024
Presenter: Chanhee Lee
Tag: Interpretable AI
Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning
GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway–pathway interaction network
ViLoN––a multi-layer network approach to data integration demonstrated for patient stratification
Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations
Presentation date: April 04, 2024
Presenter: Jun Sik Kim
DeepPINK: reproducible feature selection in deep neural networks
GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression
PAUSE: principled feature attribution for unsupervised gene expression analysis
Biologically informed deep neural network for prostate cancer discovery
Presentation date: Jan 25, 2024
Presenter: Sangyeon Shin