Princeton University researchers are gaining new insights into the causes and characteristics of diseases by harnessing machine learning to analyze molecular patterns across hundreds of diseases simultaneously.
Princeton University researchers are gaining new insights into the causes and characteristics of diseases by harnessing machine learning to analyze molecular patterns across hundreds of diseases simultaneously. Demonstrating a new tool now available to researchers worldwide, the team of computer scientists and biologists has already uncovered and experimentally confirmed previously unknown contributions of four genes to a rare form of cancer that primarily affects babies and young children.
The team, which includes collaborators at Michigan State University and the University of Oslo, introduced the system and demonstrated its abilities in a paper published in the Feb. 23 issue of the journal Cell Systems.
While previous approaches focused on genes associated with specific diseases or types of cancer, the new technique uses machine learning to find unique patterns of gene activity by looking at more than 300 different diseases simultaneously, including cancers, heart disease, metabolic disorders and many others. In doing so, it reveals distinctions between diseases and tissue types, including fine-tuned differences between related diseases that were not possible to discern with other techniques.
Image: In a demonstration of a tool for revealing molecular differences between diseases, researchers discovered four genes associated with a rare pediatric cancer. The image on the left shows a normal cell while the one on the right highlights one of the discovered genes in neuroblastoma, which afflicts babies and young children. Image courtesy of the researchers