The secrets of idiopathic pulmonary fibrosis (IPF) are written in its very name. Idiopathic refers to a disease of unknown cause, and the condition, which turns healthy lung tissue into fibrous scar tissue, still raises many questions. IPF originates at the periphery of the lung and progresses inward, compromising more and more tissue and, eventually, making it difficult for a person to breathe. There is no cure for IPF, and neither of the two drugs that are approved as treatments can reverse the scarring they only slow it down.

In a new study published June 20 in Nature Biomedical Engineering, researchers at Yale School of Medicine and collaborators took a significant step toward understanding IPF and numerous other complex diseases with an algorithm that interprets disease data and proposes treatments. The research team developed a deep generative neural network called UNAGI (unified in-silico cellular dynamics and drug screening framework) that can identify patterns in disease-specific data.

In a matter of hours or a couple of days, depending on the computer, UNAGI can learn how to glean insights from hundreds of thousands of cells, differentiating between cell types, picking out genes that are involved in disease progression, and identifying relevant regulatory networks. Then, it tries out different drugs, pulling from a long list of approved compounds to see if any of them work against whichever disease is being studied. Although UNAGI was developed with IPF data, it can also be applied to changing physical states, such as aging, and other diseases, which the researchers demonstrated using COVID data.

Source: https://medicine.yale.edu/news-article/ai-helps-researchers-understand-lung-disease-and-proposes-treatment/