CWIG-20201009 Recording

13 Oct 2020 | Presentations | Contributor(s): Durga Addepalli, Dr. Soo Lee

Nate Test Online Presentation

02 Sep 2020 | Presentations | Contributor(s): Nathan Snodgrass

This is my description.  

CEINT's NanoPHEAT Project : Integrating Exposure and Toxicity Data to Build Risk Forecasting Tools

16 Apr 2020 | Presentations | Contributor(s): Mark Wiesner, Jaleesia Amos

The NanoPHEAT tool (Nano Product Hazard and Exposure Assessment Tool) is built to compile dose-response curves from literature, including hundreds of papers, and calculates the estimated exposure to nanoparticles as released from real products, based on some in-house experimental values that we...

NanoFASE models and exposure assessment: overview, lessons learned and future developments

09 Apr 2020 | Presentations | Contributor(s): Stephen Lofts

Nano WG April 2, 2020: The H2020 NanoFASE project focused on the development of an environmental Exposure Assessment Framework for Nanomaterials. Key to the development of this framework were a number of environmental fate models: SimpleBox4Nano, a screening level multimedia model of...

The Materials Genome Initiative and the Materials Data Infrastructure

31 Mar 2020 | Presentations | Contributor(s): James Warren

Presentation on current efforts to build out the materials data infrastructure in support of the US Materials Genome Initiative and discussion on  incentives that need to be in place to realize program goals.

ISA-TAB-Nano 1.3 release review

09 Mar 2020 | Presentations | Contributor(s): Mervi Heiskanen, Mervi Anneli Heiskanen

Nano WG January 30th, 2020Review of the new 1.3 release with the Nano WG

Advances in the reliability of nanotoxicity assays

27 Feb 2020 | Presentations | Contributor(s): Elijah Petersen

Nano WG February 20, 2020Abstract: With the increasing usage of nanotechnology in consumer products, it is important to develop accurate methods to assess the potential adverse effects of engineered nanomaterials (ENMs) on humans or environmentally important organisms. However, many of these...

Nanomaterials & Scientific Committee on Consumer Safety: Overview with Focus on nano-hydroxyapatite and Silica

23 Jan 2020 | Presentations | Contributor(s): Frederick C. Klaessig

Nano WG January 23, 2020The EU’s Scientific Committee on Consumer Safety advises the European Commission on the safety of products covered by the Cosmetics Directive, which includes personal care products as well as cosmetics. In the 2013-2016 time period, the Committee published...

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

19 Dec 2019 | Presentations | 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...

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 | Presentations | 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...

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

09 Dec 2019 | Presentations | 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,...

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

09 Dec 2019 | Presentations | 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...

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

09 Dec 2019 | Presentations | 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...

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

09 Dec 2019 | Presentations | 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...

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

09 Dec 2019 | Presentations | 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...

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

06 Dec 2019 | Presentations | 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...

CAFCW109 Semi-Supervised Method for Countering Batch Effect

06 Dec 2019 | Presentations | Contributor(s): Stewart He

Predictive modeling of patient tumor growth response to drug treatment is severely limited by a lack of experimental data for training. Combining experimental data from several studies is an attractive approach, but presents two problems: batch effects and limited data for individual drugs and...

Cancer 101 plus an emphasis on Lung Cancer

05 Dec 2019 | Teaching Materials | Contributor(s): Molly Bernhard Richardson

Several culturally tailored flyers were developed for cancer screening and prevention in African American populations in the Deep South. The populations are medically underserved, from rural or urban areas, and have low socioeconomic status. These flyers accompany additional materials as...

CAFCW 104 Deep kernel Learning for Information Extraction from Cancer Pathology Reports

05 Dec 2019 | Presentations | 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...

Keynote Presentation: Computing and Data in Precision Oncology

05 Dec 2019 | Presentations | 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