A new algorithm developed by Carnegie Mellon University computer scientists, including Machine Learning Faculty, Eric Xing, has revealed for the first time how genetic networks in the fruit fly evolve during the insect's lifecycle. The discovery could lead to new cancer drug therapy, among other innovations. "Once we understand the dynamics of a network, we can build models that predict how it will respond to stimuli and identify its vulnerabilities," said Xing. "In the context of cancer genetics, for instance, this dynamic network analysis could help us identify new targets for drug therapy. For further information: http://www.cmu.edu/homepage/health/2009/summer/genetic-networking.shtml

Professors earn prestigious HP Innovation Research Awards! LTI and Machine Learning Assistant Professor, Noah Smith and ECE Professor, Greg Ganger were among 60 recipients worldwide to receive awards as part of HP's 2009 Innovation Research Program, which is designed to create opportunities for colleagues, universities and research institutes around the world to conduct breakthrough collaborative research with HP. Smith received his award for research into the use of probabilistic models to analyze political blogs, which also involves Ph.D. student Tae Yano and Associate Research Professor, William Cohen. News Release

Gene regulatory networks in cell nuclei are similar to cloud computing networks, such as Google or Yahoo!, researchers report today in the online journal Molecular Systems Biology. This finding by an international team led by Carnegie Mellon University computational biologist, Ziv Bar-Joseph helps explain not only the robustness of cells, but also some seemingly incongruent experimental results that have puzzled biologists. News Release