October 23, 2017—Researchers at Cincinnati Children’s Hospital Medical Center are combining machine learning and systems biology to distinguish disease severity in idiopathic pulmonary fibrosis (IPF), a lung disease that kills an estimated 40,000 people annually in the United States. Using
Cincinnati Children’s and the University of Cincinnati have joined together to launch the first genomics conference in the Midwest region. Precision Genomics Midwest will be held Friday, May 19 at the Kingsgate Marriott Conference Center. The conference focuses on genomic research, clinical translation, and ethics.
Scientists have harnessed the power of genomic big data and animal models of to identify the underlying causes and potential new treatments for idiopathic pulmonary fibrosis (IPF), a lung disease that kills an estimated 40,000 people annually in the United States.
The 2016 Research Annual Report of the Cincinnati Children’s Research Foundation published in January celebrates the outstanding research contributions of its 951 faculty across 52 research divisions or centers. In its 168 pages, the report details facts and figures highlighting Cincinnati Children’s accomplishments as a powerhouse in the world of pediatric research.
A new open source software package called uQIust that enables protein and RNA structure prediction, molecular simulations, and retrieval and analysis of structural data is now available to investigators. It offers a versatile, efficient, and easy-to-use toolkit for macromolecular structure exploration and analysis, supporting ultrafast clustering and model quality assessment.
With a jumble of colors resembling abstract art, this array shows the expression of genes (horizontal rows) that are associated with different types of bone marrow progenitor cells (vertical columns). Part of a study published Aug. 31 by Nature, the image was produced from a software program called Iterative Clustering and Guide-Gene Selection, developed by researchers at Cincinnati Children’s. It gives scientists an unbiased way to identify developing cells in various intermediate states and discover new cellular intermediates. Yellow indicates highly expressed genes in particular cells, while blue indicates low or no expression. The study addresses a fundamental question of developmental biology – the nature of intermediate cell states and the regulatory gene networks that cause cell-type specification.