Description: Research Blogs of Qdata Team in the University of Virignia Computer Science Department.
Toggle menu Qdata Research Summary Blogs from UVA Qdata team
Technology revolutions in the past decade have collected large-scale heterogeneous samples from many scientific domains. For instance, genomic technologies have delivered petabytes of molecular measurements across more than hundreds of types of cells and tissues from national projects like ENCODE and TCGA. Neuroimaging technologies have generated petabytes of functional magnetic resonance imaging (fMRI) datasets across thousands of human subjects (shared publicly through projects like openfMRI). Given such
Such variable graphs can significantly simplify network-driven studies about diseases, can help understand the neural characteristics underlying clinical disorders and can allow for understanding genetic or neural pathways and systems. The number of contexts (denoted as $K$) that those applications need to consider grows extremely fast, ranging from tens (e.g., cancer types in TCGA) to thousands (e.g., number of subjects in openfMRI~). The number of variables (denoted as $p$) ranges from hundreds (e.g., num