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AI/ML-Derived Whole-Genome Predictor Prospectively and Clinically Predicts Survival and Response to Treatment in Brain Cancer
- Sri Priya Ponnapalli,
- Penelope Miron,
- Kristy L. S. Miskimen,
- Kristin A. Waite,
- Nadiya Sosonkina,
- Sara E. Coppens,
- Anthony C. Bryan,
- Estevan P. Kiernan,
- Huanming Yang,
- Jay Bowen,
- Ghunwa A. Nakouzi,
- Jill S. Barnholtz-Sloan,
- Andrew E. Sloan,
- Tiffany R. Hodges,
- Orly Alter
Optimized Patient-Specific Catheter Placement for Convection-Enhanced Nanoparticle Delivery in Recurrent Glioblastoma
- Chengyue Wu,
- David A. Hormuth,
- Chase Christenson,
- Ryan T. Woodall,
- Michael R. A. Abdelmalik,
- William T. Phillips,
- Thomas J. R. Hughes,
- Andrew J. Brenner,
- Thomas E. Yankeelov
Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction
- Oleksandr Narykov,
- Yitan Zhu,
- Thomas Brettin,
- Yvonne Evrard,
- Alexander Partin,
- Maulik Shukla,
- Priyanka Vasanthakumari,
- James Doroshow,
- Rick Stevens
This work studies a particular setting for regression problems – tasks with complex combinatorial data space where samples can be divided into distinct groups. Anti-cancer drug response prediction is a perfect example of this setting, in which each ...
Scalable Lead Prediction with Transformers using HPC resources
A promising direction in cancer drug discovery is high-throughput screening of extensive compound datasets to identify advantageous properties, including their ability to interact with relevant biomolecules such as proteins. However, traditional ...
Automated Whole-Body Tumor Segmentation and Prognosis of Cancer on PET/CT
Automatic characterization of malignant disease is an important clinical need to facilitate early detection and treatment of cancer. A deep semi-supervised transfer learning approach was developed for automated whole-body tumor segmentation and prognosis ...
Index Terms
- Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis