Version 1
: Received: 21 January 2020 / Approved: 24 January 2020 / Online: 24 January 2020 (10:26:26 CET)
How to cite:
M. K. Nassief, A. Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine. Preprints2020, 2020010274. https://doi.org/10.20944/preprints202001.0274.v1
M. K. Nassief, A. Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine. Preprints 2020, 2020010274. https://doi.org/10.20944/preprints202001.0274.v1
M. K. Nassief, A. Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine. Preprints2020, 2020010274. https://doi.org/10.20944/preprints202001.0274.v1
APA Style
M. K. Nassief, A. (2020). Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine. Preprints. https://doi.org/10.20944/preprints202001.0274.v1
Chicago/Turabian Style
M. K. Nassief, A. 2020 "Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine" Preprints. https://doi.org/10.20944/preprints202001.0274.v1
Abstract
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.
bioinformatics; computational genomics; computational medicine; data science; data visualization; parallel processing; grid computing; fog computing
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
