How AI Algorithm Predicts Viral Infections in Patients By Using Gene Expression Data

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A team of researchers has used an Artificial Intelligence (AI) algorithm to sift through terabytes of gene expression data to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS, and swine flu. The researchers, including Pradipta Ghosh from the University of California San Diego, indicated two telltale signatures. One, a set of 166 genes, reveals how the human immune system responds to viral infections. The second set of 20 signature genes predicts the severity of a patient’s disease. For example, the need to hospitalize or use a mechanical ventilator. The algorithm’s utility was validated using lung tissues collected at autopsies from deceased patients with Covid-19 and animal models of the infection.

“These viral pandemic-associated signatures tell us how a person’s immune system responds to a viral infection and how severe it might get, and that gives us a map for this and future pandemics," said Ghosh.


Sometimes, though, the body releases too many cytokines, creating a runaway immune system that attacks its healthy tissue, indicates the study published in the journal biomedicine.



Each time a new set of data from patients with Covid-19 became available, the team tested it in their model. They saw the same signature gene expression patterns every time.

By examining the source and function of those genes in the first signature gene set, the study also revealed the source of cytokine storms — the cells lining lung airways and white blood cells known as macrophages and T cells.

In addition, the results illuminated the consequences of the storm — damage to those same lung airway cells and natural killer cells, a specialized immune cell that kills virus-infected cells.


To test their theory, the team pre-treated rodents with either a precursor version of Molnupiravir, a drug currently being tested in clinical trials for the treatment of Covid-19 patients, or SARS-CoV-2-neutralising antibodies.


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