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...

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...

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,...

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...

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...

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...

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...

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...

CAFCW109 Semi-Supervised Method for Countering Batch Effect

06 Dec 2019 | 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 | 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 | 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 | 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

Global Harmonization of Nanoinformatics: A Case Study in Convergence and Team Science

27 Nov 2019 | Contributor(s): Frederick C. Klaessig, Christine Ogilvie Hendren

Nano WG November 14, 2019National Nanotechnology Coordination Office (NNCO) sponsored Nanoinformatics presentation followed by discussion on Nanomaterial safety data integration with eNanoMapper database and eNanoMapper Ontology...

Read across in nanosafety research: Dissolution behaviour of a library of 37 nanomaterials in simplified physiological media

05 Nov 2019 | Contributor(s): Tassos Papadiamantis, Emily Guggenheim, Antreas Afantitis, Sophie Briffa, Georgia Melagraki, Iseult Lynch, Eugenia Valsami-Jones

NanoWorkingGroup, 31 October 2019The dissolution of engineered nanomaterials (ENMs) is a complex process, which remains poorly understood despite being one of the key prerequisites for the risk assessment of ENMs, and having a direct influence on toxicological and eco-toxicological effects....

Examples on Using Wikidata to Support Nanoinformatics Projects

13 Sep 2019 | Presentations | Contributor(s): Egon Willighagen

Nano WG Meeting September 12, 2019 Two years ago, a community survey resulted in a sketch of a linked data approach to nanosafety research [0]. Interoperability of linked data enables the reuse, but linking databases is not trivial. Many resources are simply not FAIR enough for reuse yet,...

Health Information National Trends Survey (HINTS) 5 Cycle 2 (2018)

02 Aug 2019 | Downloads | Contributor(s): Kelly Blake, Richard Moser

The Health Information National Trends Survey (HINTS) is a nationally representative survey that has been administered every few years by the National Cancer Institute since 2003. The HINTS target population is all adults aged 18 or older in the civilian non-institutionalized population of the...

Some thoughts on our progress in defining and advancing nanoinformatics

01 Aug 2019 | Presentations | Contributor(s): Mark D Hoover

Nano WG Meeting November 1, 2018Discussion on Nano WG accomplishments and future directions.

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

26 Jun 2019 | Downloads | Contributor(s): Anil Wali

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

Geant4 - A Simulation Toolkit

20 Jun 2019 | Tools | Contributor(s): Sachin Dev

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Jaqpot - An open-source web platform for creating, using, testing and sharing predictive models in nano-informatics

20 Jun 2019 | Presentations | Contributor(s): Haralambos Sarimveis

Nano WG June 20, 2019Computational models that predict physicochemical properties, biokinetics or adverse biological effects of Engineered Nanomaterials (ENMs) are becoming increasingly important to support risk assessment and safety-by-design. This is primarily due to cost-saving and...