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Getting poster data...
Christoph C.H. Langer, Michael Mitter, Daniel W. Gerlich (Dr.-Bohr-Gasse 3)
Genomics experiments generate enormous amounts of highly complex data. Extracting relevant information from such data requires detailed knowledge about the underlying biology as well as mastering technically challenging computational methodology. Analysis and interpretation of genomics data are hence often split between biologists and bioinformaticians, respectively. To allow life science experts take back control of their data and ask biologically meaningful questions without the need to consult with programming experts, we present HiCognition, a computational framework that combines interactive data exploration with advanced machine learning. In HiCognition, a user can fluidly explore and automatically detect sets of genomic regions of interest, such as genes, topologically associating domains, or chromatin compartments based on multi-dimensional features including chromosome conformation, chromatin composition, and other genomics data. HiCognition thereby accelerates scientific discovery by enabling fast cycles of hypothesis generation and testing in the hand of a single user.