Artificial intelligence identifying new potentially deadly COVID-19 variants

By Stephen Beech via SWNS

Artificial intelligence is being used to identify potentially deadly new COVID-19 variants much quicker than traditional methods.

Mathematicians at The Universities of Manchester and Oxford have developed an AI framework that can identify and track emerging forms of the virus that triggered the global pandemic.

And they say the method could help with other infections in the future.

The framework combines dimension reduction techniques and a new explainable clustering algorithm called CLASSIX, developed by mathematicians at The University of Manchester.

It enables the quick identification of groups of viral genomes that might present a risk in the future from huge volumes of data.

Scientists say their findings, published in the journal PNAS, could support traditional methods of tracking viral evolution.

Study first author Dr. Roberto Cahuantzi, a researcher at The University of Manchester, said: “Since the emergence of COVID-19, we have seen multiple waves of new variants, heightened transmissibility, evasion of immune responses, and increased severity of illness.

“Scientists are now intensifying efforts to pinpoint these worrying new variants, such as alpha, delta and omicron, at the earliest stages of their emergence.

"If we can find a way to do this quickly and efficiently, it will enable us to be more proactive in our response, such as tailored vaccine development and may even enable us to eliminate the variants before they become established.”

He explained that, like many other RNA viruses, COVID-19 has a high mutation rate and short time between generations meaning it evolves extremely rapidly.

It means that identifying new strains that are likely to be problematic in the future requires considerable effort.

Currently, there are almost 16 million sequences available on the GISAID database (the Global Initiative on Sharing All Influenza Data), which provides access to genomic data of flu viruses.

Mapping the evolution and history of all COVID-19 genomes from the data is currently done using massive amounts of computer and human time.

Dr. Cahuantzi says the new method allows automation of such tasks.

The researchers processed 5.7 million high-coverage sequences in only one to two days on a standard modern laptop.

Dr. Cahuantzi says that would not be possible for existing methods, to put the identification of concerning pathogen strains in the hands of more researchers due to reduced resource needs.

Professor Thomas House, of The University of Manchester, said: “The unprecedented amount of genetic data generated during the pandemic demands improvements to our methods to analyze it thoroughly.

"The data is continuing to grow rapidly, but without showing a benefit to curating this data, there is a risk that it will be removed or deleted.

“We know that human expert time is limited, so our approach should not replace the work of humans altogether but work alongside them to enable the job to be done much quicker and free our experts for other vital developments.”

The proposed method works by breaking down genetic sequences of the COVID-19 virus into smaller “words” (called 3-mers) represented as numbers by counting them. It then groups similar sequences together based on their word patterns using machine learning techniques.

Stefan Güttel, of the University of Manchester, said: “The clustering algorithm CLASSIX we developed is much less computationally demanding than traditional methods and is fully explainable, meaning that it provides textual and visual explanations of the computed clusters.”

Dr. Cahuantzi added: “Our analysis serves as a proof of concept, demonstrating the potential use of machine learning methods as an alert tool for the early discovery of emerging major variants without relying on the need to generate phylogenies.

“Whilst phylogenetics remains the ‘gold standard’ for understanding the viral ancestry, these machine learning methods can accommodate several orders of magnitude more sequences than the current phylogenetic methods and at a low computational cost.”