Artificial intelligence are giving their service in medical terms, today as we are upgrading in field of technology our medical recruitments are upgrading.
Researchers at the University of California San Diego have created a tool that allows glycomics datasets to be analyzed using explainable Artificial Intelligence (AI) systems and other machine learning approaches, called GlyCompare. The study was published in Nature Communications.
“We applied GlyCompare to cancer tissues and showed that while one couldn’t find cancer-specific glycans using standard statistical methods, novel biomarkers emerge when processed using our method,” said Nathan Lewis, corresponding author.
In another analysis, the team showed the method substantially boosts statistical power, such that one needs half as many samples to get equivalent power to detect biomarkers. In the paper, the researchers outline how the methods behind GlyCompare will be transformative for bringing glycomics to the clinic.
One of the keys to the GlyCompare approach is that it looks at the biological steps needed to synthesize the subunits that make up glycans, rather than just looking at only the whole glycans themselves, greatly improving the accuracy of statistical analyses of glycomics data. The researchers believe this approach will enable the detection of more subtle changes in glycosylation in many applications, including early-stage cancer. Moreover, GlyCompare could lead to new insights on the mechanisms behind the observed changes in glycans that are present.