Bio-Viruses Prediction using AI and Genome Data

Created by team Ameen AI on مارس 14, 2024

This comprehensive study leveraged the capabilities of artificial intelligence by training a powerful Long Short-Term Memory (LSTM) neural network model. The researchers ensured the model was provided with a vast genomic dataset spanning over 100 years of viral genetic information collected since 1919. This large collection of past viral genome sequences, accumulated over a century of viral evolution and circulation, was utilized to train the LSTM model. By analyzing the genetic patterns and sequences encoded within this extensive database, the model was able to predict trends in COVID-19 virus spread and also forecast potential mutations that could lead to new viral strains in the future. In addition to learning the inherent patterns of viral genetic development over time, the LSTM network was notably able to uncover exogenous manipulation factors that could accelerate or influence the emergence of novel viruses. This demonstrated the significant potential that advanced AI applications like LSTM models have for the in-depth reverse engineering of viruses and predictive viral analytics. With its abilities to digitally anticipate dangerous future mutant strains before they occur, such viral foresight powered by artificial intelligence could play a pivotal role in safeguarding global societies from future threats. On a larger scale, the incorporation of analogous high-powered predictive technologies within urban centers could help transform modern cities into more cognitive environments with enhanced health monitoring systems and initiatives to continuously improve citizens' quality of life. My research paper link: https://www.sciencedirect.com/science/article/pii/S2352186421001796?via%3Dihub https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1163764

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