Source: Stanford University Medical Center
Summary: Researchers describe an algorithm they’ve developed that automates the most labor-intensive part of genetic diagnosis: that of matching a patient’s genetic sequence and symptoms to a disease described in the scientific literature.
Today, diagnosing rare genetic diseases requires a slow process of educated guesswork. Researchers from the Stanford University Medical Center describe an algorithm they’ve developed that automates the most labor-intensive part of genetic diagnosis: that of matching a patient’s genetic sequence and symptoms to a disease described in the scientific literature. Without computer help, this match-up process takes 20-40 hours per patient: The expert looks at a list of around 100 of the patient’s suspicious-looking mutations, makes an educated guess about which one might cause disease, checks the scientific literature, then moves on to the next one. The algorithm developed by the team cuts the time needed by 90%. The study findings were published in the journal Genetics in Medicine.
The algorithm’s name, Phrank – a mashup of “phenotype” and “rank” – hints at how it works: Phrank compares a patient’s symptoms and gene data to a knowledge base of medical literature, generating a ranked list of which rare genetic diseases are most likely to be responsible for the symptoms. The clinician has a logical starting point for making a diagnosis, which can be confirmed with one to four hours of effort per case instead of 20-40 hours. The mathematical workings of Phrank aren’t tied to a specific database, a first for this type of algorithm. This makes it much more flexible to use. Phrank also dramatically outperforms earlier algorithms that have tried to do the same thing. Prior studies had tested algorithms on made-up patients instead because real-patient data for this research is hard to come by.
More Information: Karthik A. Jagadeesh et al, Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization”, Genetics in Medicine (2018). DOI: 10.1038/s41436-018-0072-y