- Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
- Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
- Accurate In Silico Prediction of Ligand Binding Potency in Therapeutic Targets using Quantum Molecular Design
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Junction Tree Variational Autoencoder for Molecular Graph Representation
- A Comprehensive Survey on Graph Neural Network
- Machine Learning-Enhanced T Cell Neoepitope Discovery for Immunotherapy Design
- Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space
- The Hallmarks of Cancer
- Hallmarks of Cancer: The Next Generation
- Artificial neural networks for immunological recognition
- TERT promoter wild-type glioblastomas show distinct clinical features and frequent PI3K pathway mutations
- Targeting CD133 antigen in cancer
- Diagnostic and Therapeutic Biomarkers in Glioblastoma: Current Status and Future Perspectives.
- Stem cell signature in glioblastoma: therapeutic development for a moving target
- Glioblastoma: pathology, molecular mechanisms and markers