Tags: #supercomputing

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  1. CAFCW115 Massively Parallel Large-Scale Multi-Model Simulation of Tumor Development including Treatments

    19 Dec 2019 | Contributor(s):: Marco Berghoff, Jakob Rosenbauer, Alexander Schug

    The temporal and spatial resolution in the microscopy of tissues has increased significantly within the last years, yielding new insights into the dynamics of tissue development and the role of the single-cell within it. A thorough theoretical description of the connection of single-cell...

  2. CAFCW117 A Scalable, Validated Platform for Generative Lead Optimization of De Novo Molecules: Case Study in Discovery of Potent, Selective Aurora Kinase Inhibitors with Favorable Secondary Pharmacology

    19 Dec 2019 | Contributor(s):: Andrew Weber

    De Novo design of therapeutic agents is currently a slow, expensive process generally relying on a large high throughput screen and several follow up cycles of iterative design to enhance the potency, eliminate safety liabilities, and enable favorable pharmacokinetic behavior. Computer aided drug...

  3. CAFCW 122 Fusion of Structure Based Deep Learning to Accelerate Molecular Docking Predictions

    09 Dec 2019 | Contributor(s):: Derek Jones

    Modeling interactions with biological targets is a necessary step to begin reasoning about the therapeutic potential of a novel molecule in the drug discovery process. Molecular docking aids drug discovery researchers by searching over potential binding ‘poses’ of a drug molecule,...

  4. CAFCW 104 Deep Kernel Learning for Information Extraction from Cancer Pathology Reports

    09 Dec 2019 | Contributor(s):: Devanshu Agrawal, Abhishek Dubey, Georgia Tourassi, Jacob Hinkle

    Cancer pathology reports comprise a rich source of data for surveilling cancer incidents and tracking cancer trends across the United States. Cancer registries manually extract key pieces of information from these reports including tumor site, histology, laterality, behavior, grade, and...

  5. CAFCW 105 Acceleration of Hyperparameter Optimization via Task Parallelism for Information Extraction from Cancer Pathology Reports

    09 Dec 2019 | Contributor(s):: John Gounley, Hong-Jun Yoon

    Recent advances in high-performance computing systems for artificial intelligence enable large-scale training of information extraction models from free-form natural language texts. The development of these models is essential to the cancer surveillance research and automation. In this study, we...

  6. CAFCW 120 Integrating High-Performance Simulations and Learning toward Improved Cancer Therapy

    09 Dec 2019 | Contributor(s):: Austin Clyde, Dave Wright, Shantenu Jha

    We develop a novel deep learning workflow to effectively combine expensive but accurate molecular dynamics (MD) based BFE calculations with fast machine learning models to predict the affinity of compounds. In this approach, candidates are sampled from a large billion-compound synthetically...

  7. CAFCW 113 Digital Twins for Predictive Cancer Care: an HPC-Enabled Community Initiative

    09 Dec 2019 | Contributor(s):: Emily Greenspan, Carolyn Lauzon, Amy Gryshuk, Jonathan Ozik, Nicholson Collier, Tanveer Syeda-Mahmood, Ilya Shmulevich, Tina Hernandez-Boussard, Paul Macklin

    Cancer is a complex multiscale dynamical systems problem with interactions between the tumor and host at the molecular, cellular, tissue, and organism levels. Moreover, treatment occurs within a larger dynamical system that couples clinical care teams, hospital systems, industry, and government...

  8. CAFW110 Machine Learning Algorithms in Histology and Radiology for Cancer Drug Discovery and Development

    06 Dec 2019 | Contributor(s):: Partha Paul, Yuki Shimahara, Daisaku Takamiya

    Background: Lung cancer is one of the most common cancers in the world. It is a leading cause of cancer death in men and women in the United States. Computational approaches such as deep learning could help accurate and efficient analysis of biomarkers, both histopathology and radiology, to...

  9. Keynote Presentation: Computing and Data in Precision Oncology

    05 Dec 2019 | Contributor(s):: Warren Kibbe

    Computing and Data in Precision OncologyWarren A Kibbe, Ph. D.Professor, Biostats & BioinformaticsChief Data Officer, Duke Cancer Institutewarren.kibbe@duke.edu@supercomputing#sc19#CAFCW19#ComputationalPhenomics#PrecisionOncology