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Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory
Ahmad M Abu Haimed1, Tanzila Saba1, Ayman Albasha1
1Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia.
Environmental Technology & Innovation
|April 7, 2021
Summary
This study uses AI and big data to analyze SARS-CoV-2 evolution, identifying shared amino acids and predicting future mutations. Reverse engineering reveals rapid mutation rates and viral similarities across families.
Area of Science:
- Virology and Bioinformatics
- Artificial Intelligence in Disease Research
Background:
- Understanding viral evolution is critical for predicting and controlling outbreaks.
- Previous studies have analyzed viral families, but a comprehensive reverse engineering approach for SARS-CoV-2 evolution is needed.
- The emergence of SARS-CoV-2 necessitates advanced analytical methods to track its patterns and predict future behavior.
Purpose of the Study:
- To apply reverse engineering with Artificial Intelligence (AI) and big data to understand SARS-CoV-2 patterns and evolution.
- To compare SARS-CoV-2 with historical viral families (Orthomyxoviridae, Retroviridae, Filoviridae, Flaviviridae, Coronaviridae) over the past century.
- To predict future SARS-CoV-2 mutations using AI and phylogenic tree data.
Main Methods:
- Utilized a reverse engineering approach incorporating AI and big data analytics.
- Analyzed amino acid similarities, particularly active sites (S, L, T), across five major viral families and SARS-CoV-2.
- Developed a mathematical formula to quantify viral evolution difference percentages based on phylogenic trees.
- Employed Long Short-term Memory (LSTM) networks for predicting viral evolution using phylogenic data.
- Focused prediction on the ORF7a protein of SARS-CoV-2 as an initial step.
Main Results:
- SARS-CoV-2 shares key active amino acids (S, L, T) with Orthomyxoviridae, Retroviridae, Filoviridae, Flaviviridae, and Coronaviridae.
- A novel mathematical formula quantifies viral evolution differences, revealing SARS-CoV-2's rapid mutation rate relative to its emergence.
- AI, specifically LSTM, successfully predicted potential future evolved instances of SARS-CoV-2 based on phylogenic data.
Conclusions:
- Reverse engineering, AI, and big data provide effective tools for analyzing viral evolution and predicting mutations.
- SARS-CoV-2 exhibits accelerated evolutionary behavior compared to historical viral families.
- The study successfully demonstrated a systematic approach to predicting viral mutations, starting with specific protein analysis (ORF7a).
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