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Cryo-electron tomography enables 3D visualization of cells in a near native state at molecular resolution. The produced cellular tomograms contain detailed information about all macromolecular complexes, their structures, their abundances and their specific spatial locations in the cell. However, extracting this information is very challenging and current methods usually rely on templates of known structure. Here, we formulate a template-free visual proteomics analysis as a de novo pattern mining problem and propose a new framework called Multi Pattern Pursuit for supporting proteome-scale de novo discovery of macromolecular complexes in cellular tomograms without using templates of known structures. Our tests on simulated and experimental tomograms show that our method is a promising tool for template-free visual proteomics analysis.
Contrary to long-held views, recent evidence indicates that $textit{de novo}$ birth of genes is not only possible, but is surprisingly prevalent: a substantial fraction of eukaryotic genomes are composed of orphan genes, which show no homology with a
RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of RNA structure-function relationship. One of the main challenges in t
A multiscale mathematical model is presented to describe the de novo granulation and the evolution of multispecies granular biofilms within a continuous reactor. The granule is modelled as a spherical free boundary domain with radial symmetry. The eq
Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging a
Motivation: Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target decoy approaches (TDAs) and decoy-free approaches (DFAs), have been