Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Amazon (1)
- Amazon.com (Firm) (1)
- Artificial image object (1)
- Artificial intelligence (1)
- Breast cancer biomarker classification (1)
-
- Cloud computing (1)
- Cluster (1)
- Convolutional neural network (1)
- Diagnostic imaging – Computer simulations (1)
- EC2 (1)
- Glioblastoma multiforme – Treatment (1)
- Gliomas – Treatment (1)
- Gold nanoshells (1)
- Image classification (1)
- Lasers (1)
- Machine learning algorithm (1)
- Macrophages (1)
- Monte Carlo method (1)
- Nodes (1)
- Photothermal ablation (1)
- RNA sequencing (1)
- Radiotherapy (1)
- Radiotherapy – Computer simulations (1)
- Simulation (1)
- Superparamagnetic iron oxide nanoparticles (1)
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
School of Medicine Faculty Publications
Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …
Gate Monte Carlo Simulations In A Cloud Computing Environment, Blake Austin Rowedder
Gate Monte Carlo Simulations In A Cloud Computing Environment, Blake Austin Rowedder
UNLV Theses, Dissertations, Professional Papers, and Capstones
The GEANT4-based GATE is a unique and powerful Monte Carlo (MC) platform, which provides a single code library allowing the simulation of specific medical physics applications, e.g. PET, SPECT, CT, radiotherapy, and hadron therapy. However, this rigorous yet flexible platform is used only sparingly in the clinic due to its lengthy calculation time. By accessing the powerful computational resources of a cloud computing environment, GATE's runtime can be significantly reduced to clinically feasible levels without the sizable investment of a local high performance cluster. This study investigated a reliable and efficient execution of GATE MC simulations using a commercial cloud …
Macrophages Loaded With Gold Nanoshells For Photothermal Ablation Of Glioma: An In Vitro Model, Amani Riad Makkouk
Macrophages Loaded With Gold Nanoshells For Photothermal Ablation Of Glioma: An In Vitro Model, Amani Riad Makkouk
UNLV Theses, Dissertations, Professional Papers, and Capstones
The current median survival of patients with glioblastoma multiforme (GBM), the most common type of glioma, remains at 14.6 months despite multimodal treatments (surgery, radiotherapy and chemotherapy). This research aims to study the feasibility of photothermal ablation of glioma using gold nanoshells that are heated upon laser irradiation at their resonance wavelength. The novelty of our approach lies in improving nanoshell tumor delivery by loading them in macrophages, which are known to be recruited to gliomas via tumor-released chemoattractive agents. Ferumoxides, superparamagnetic iron oxide (SPIO) nanoparticles, are needed as an additional macrophage load in order to visualize macrophage accumulation in …