MedThaiVis: An approach for thai biomedical data visualization

2017 ◽  
Author(s):  
Jarernsri Mitrpanont ◽  
Nut Janekitiworapong ◽  
Supakorn Ongsritrakul ◽  
Supatsara Varasai
Keyword(s):  
Data Visualization ◽  
Biomedical Data

scholarly journals Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine

2020 ◽  
Author(s):  
Andrew Kamal
Keyword(s):  
Parallel Processing ◽  
Grid Computing ◽  
Data Visualization ◽  
Data Science ◽  
Biomedical Data ◽  
Research Opportunities ◽  
Adversarial Network ◽  
Visualization Software ◽  
Computing Approach ◽  
Processing Architecture

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

scholarly journals Use ggbreak to Effectively Utilize Plotting Space to Deal With Large Datasets and Outliers

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuangbin Xu ◽  
Meijun Chen ◽  
Tingze Feng ◽  
Li Zhan ◽  
Lang Zhou ◽  
...  
Keyword(s):  
Data Visualization ◽  
Open Source Software ◽  
Large Scale ◽  
Extreme Values ◽  
Data Distribution ◽  
Visual Space ◽  
R Package ◽  
Large Datasets ◽  
Sequential Data ◽  
Biomedical Data

With the rapid increase of large-scale datasets, biomedical data visualization is facing challenges. The data may be large, have different orders of magnitude, contain extreme values, and the data distribution is not clear. Here we present an R package ggbreak that allows users to create broken axes using ggplot2 syntax. It can effectively use the plotting area to deal with large datasets (especially for long sequential data), data with different magnitudes, and contain outliers. The ggbreak package increases the available visual space for a better presentation of the data and detailed annotation, thus improves our ability to interpret the data. The ggbreak package is fully compatible with ggplot2 and it is easy to superpose additional layers and applies scale and theme to adjust the plot using the ggplot2 syntax. The ggbreak package is open-source software released under the Artistic-2.0 license, and it is freely available on CRAN (https://CRAN.R-project.org/package=ggbreak) and Github (https://github.com/YuLab-SMU/ggbreak).

scholarly journals Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine

2020 ◽  
Author(s):  
Andrew M. K. Nassief
Keyword(s):  
Parallel Processing ◽  
Grid Computing ◽  
Data Visualization ◽  
Data Science ◽  
Biomedical Data ◽  
Research Opportunities ◽  
Adversarial Network ◽  
Visualization Software ◽  
Computing Approach ◽  
Processing Architecture

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

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