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In the wake of the deadly coronavirus pandemic, scientists at the University of California, Berkeley are exploring the benefits of medical algorithms and pharmacological interventions to treat a potentially fatal condition.

The researchers have developed a novel drug-based therapy that treats the signs and symptoms of myalgic encephalomyelitis/chronic fatigue syndrome, or ME/CFS, by using computer algorithms to predict which patients respond best to treatment.

They are also developing an algorithm for determining whether a treatment works on a person’s genetic makeup.

The study, published in the Journal of Neural Engineering, builds on research done at the U.S. National Institutes of Health and the University.

The authors of the paper are UC Berkeley graduate student and co-lead author Andrew H. Krashen, who has worked on the project with UC Berkeley postdoctoral fellow Jia Wang, and UC Berkeley professor of computer science and engineering, Dr. Jia Wu.

They developed a drug-guided algorithm that uses a novel computer model that can predict the response to different treatments, including drug therapy and lipid therapy.

They also developed an algorithm that assesses whether a person has a genetic predisposition for ME/Cs, based on whether they are Caucasian or African American.

The algorithm was designed to predict the best response to treatment based on the patients genetic makeup and on the amount of disease symptoms they experience.

“The goal of this study is to identify a new class of drugs that can help to treat people with chronic fatigue syndrome and other forms of ME/ CFS,” Krasen said.

“We think this new drug class is much more effective than the existing treatments available for ME, so it could be very important in the future for the treatment of this complex illness.”

The researchers developed the algorithm using computer modeling, data collected from ME/cfs patients, and human clinical trials.

They have developed software to evaluate which patients responded best to different treatment options and were the most likely to respond well to the drug-like treatments, which were then used to determine which patients could benefit from further research and development.

The data was collected through a large sample of individuals who have ME/ cfs, and the researchers used this data to build an algorithm based on a patient’s genetic characteristics to predict whether the drug could work on a particular patient.

The findings of the study have the potential to lead to a whole new class for drugs, Krashe said.

“Our algorithms are based on clinical trials that have shown that this drug works very well on patients with a very low prevalence of these symptoms,” Kashen said.

The results could have far-reaching implications for people with ME/ cysts and other chronic conditions, he added.

“This is a very exciting development that could lead to new treatments and therapies that are more effective at treating the symptoms and the disease in patients with these diseases than currently available treatments,” Kbashen said, “and potentially lead to more effective treatments for people in this group.”

The study is the first of its kind and the first to use computer models to predict a patient response to a treatment.

It is also the first study to test the efficacy of a drug in treating the signs of ME.

“In general, we don’t know if this type of drug will be effective for patients with other chronic illnesses,” Kvashen said in a statement.

“It will take a lot of time to find out if the new treatment will be as effective as the existing drugs, so we hope that this study will advance the field and help to advance the understanding of how to treat patients with ME.”

Follow Miriam Kramer on Twitter: @mikebakermike

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