Human DNA AI Model to Predict COVID-19 Symptomatic or Asymptomatic Percentages

Abstract

Peter Savier Oropeza-Martínez, Luis Manuel Gaggero-Sager*, Haydeé Rosas-Vargas

The current paper proposes to use convolutional neural networks (CNN) to analyze human genome Single Nucleotide Variants (SNVs) from nuclear Deoxyribonucleic Acid (DNA) and Mitochondrial Deoxyribonucleic Acid (mtDNA) presented as a 2D image structure to understand if the answer to COVID-19 severities can be found in the human genome. That methodology was implemented with 447 Mexican population samples. From results two main groups were formed divided in symptomatic and asymptomatic cases composed by 80.986% and 19.014% respectively and the model was validated through an online survey of individuals, giving a 91.89% of accuracy.

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