A1261
Title: Non-euclidean statistics for learning structural biology
Authors: Stephan Huckemann - University of Goettingen (Germany) [presenting]
Abstract: The problem of predicting ribonucleic acid (RNA) structure geometry at the suite level (from one base to one of its adjacent bases) based on experimental maps of electron density is addressed through two learning methods: CLEAN for clash removal and RNAprecis for high-detail prediction. This requires creating ground truth gold standard databases of manually corrected suites. From these, structure clusters are identified by MINT-AGE, an adaptive iterative linkage preclustering method on metric spaces (AGE), mapping each precluster to a stratified sphere. Dimension reduction is achieved by a variant of principal nested spheres (PNS) analysis combined with mode hunting (using the variant WiZer of the SiZer) on the resulting one-dimensional torus (MINT). The power of both methods is illustrated, and notably, MINT-AGE clusters show high agreement with curated gold standard suite conformers, sometimes even finer.
