George Washington University researchers, including Dr. Keith A. Crandall, director of the Computational Biology Institute at George Washington University Milken Institute School of Public Health (Milken Institute SPH), worked with AMPEL BioSolutions, a genomics technology company, to train a computer to analyze genetic and patient data to predict whether an individual living with lupus was experiencing a flare in disease activity.
The insights achieved by the combination of bioinformatics and genomics data through machine learning could help the way doctors treat chronic diseases like lupus, according to the paper, which was published in the journal Nature Scientific Reports.
The genetic analysis, called gene expression analysis, examines the number and pattern of genes expressed at a single moment and can provide insight into various genomic abnormalities. Physicians one day may be able to use information gathered by a lab test and analyzed by this approach to predict flares and provide early treatment, saving patients from unnecessary pain and other symptoms.
“This work is a great example of the insights gained by using artificial intelligence such as machine learning approaches to analyze diverse types of data, in this case integrating clinical data with genomics, to make effective and individualized insights on patient health,” said Dr. Crandall, who is also a professor of biostatistics and bioinformatics at Milken Institute SPH.Friday Letter Submission, Publish on July 26