Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine release_rev_6e22e33f-c7f6-4fe5-b29d-301e7f2b8abb

by Andrew Kamal

Released as a post by Center for Open Science.

2020  

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.
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Date   2020-01-21
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