Surabaya: artificial intelligence May help predict mutations in COVID-19. Some have achieved perfect strike rates.
The changes of Kovid-19 continue to plague the world. For medical scientists, getting an early look at when and how the virus changes is important—it gives them a head start on developing the latest and strongest batches of vaccines and drugs, and the most important to consider what health directives might be needed. Time is available. issued
While speed and accuracy are important, Machine learning And artificial intelligence (AI) comes to the fore. AI can analyze data with speed and accuracy unmatched by humans. And it’s starting to help in the fight against Covid-19, severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome.MERS), a triad of viral infections that can be fatal.
DNA analysis of viruses has long been used to uncover similarities between diseases, helping to draw links that unlock more knowledge about each. COVID-19, SARS and MERS share very similar nucleotide sequences, making them prime candidates for cross-reference and study together. Effectively, they are from the same family.
But research conducted without machine learning or AI has not made the progress needed to crack the code and understand how COVID-19 might mutate. In the trial, researchers studied 30 DNA samples each from COVID-19, MERS and SARS, compared to the ‘primer’ of COVID-19. A primer is a DNA sequence that is used to test whether a DNA sample is positive for a specific virus or bacteria by analyzing the similarities between the sample and the primer. polymerase chain reaction (PCR) test revealed differences between the nucleotide structures of the three viruses, but the results did not definitively distinguish between COVID-19 and the other two viruses.
Adding machine learning to the equation has drastically increased the success rate. It allows researchers to study the spacing patterns of each DNA sample, to identify and predict the exact location of DNA infected with COVID-19. The researchers used four machine-learning methods, each optimized with a different combination of parameter settings. The diversity allowed the researchers to focus on the best prediction results for each case study.
Equality of DNA structure COVID-19, MERS and SARS are among the obstacles to predicting which samples are actually infected with COVID-19. The DNA-alignment method with the COVID-19 primary samples resulted in positive values in all samples, including both MERS and SARS. But with the help of AI, it has become much clearer where the difference between viruses lies. Machine learning can distinguish between three closely related viruses in a way that DNA testing cannot.
The prediction results were robust, indicating that the two machine learning optimization approaches were able to observe changes in DNA alignment patterns and predict shifts with 100 percent accuracy. Two less successful optimizations still produced 98.3 percent accuracy, with errors in the Covid-19 sample data. This indicates that the DNA composition in the COVID-19 samples is still diverse and it is likely that mutations will continue to occur.
This data is extremely useful for researchers and pharmaceutical companies. The results of this analysis provide the clearest indication yet of how Covid-19 will evolve, allowing for better planning on key resourcing decisions such as vaccine production and production of antivirals. As the pandemic continues to grow, the research community needs to stay on the cutting edge to give the world a fighting chance against the coronavirus. AI in the research process helps to do that.
