🍩 Database of Original & Non-Theoretical Uses of Topology

(found 3 matches in 0.001483s)

  1. Topological Data Analysis for Genomics and Evolution: Topology in Biology (2019)

    Raul Rabadan, Andrew J. Blumberg
    Abstract Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.

  2. Disease Model of GATA4 Mutation Reveals Transcription Factor Cooperativity in Human Cardiogenesis (2016)

    Yen-Sin Ang, Renee N. Rivas, Alexandre JS Ribeiro, Rohith Srivas, Janell Rivera, Nicole R. Stone, Karishma Pratt, Tamer MA Mohamed, Ji-Dong Fu, C. Ian Spencer, Nathaniel D. Tippens, Molong Li, Anil Narasimha, Ethan Radzinsky, Anita J. Moon-Grady, Haiyuan Yu, Beth L. Pruitt, Michael P. Snyder, Deepak Srivastava

  3. HiDeF: Identifying Persistent Structures in Multiscale ‘Omics Data (2021)

    Fan Zheng, She Zhang, Christopher Churas, Dexter Pratt, Ivet Bahar, Trey Ideker
    Abstract In any ‘omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.