Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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. Preprints 2020, 2020010274 (doi: 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 (doi: 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.

Supplementary and Associated Material

Subject Areas

bioinformatics; computational genomics; computational medicine; data science; data visualization; parallel processing; grid computing; fog computing

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