rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Genome Clustering
    sub title: From Linguistic Models to Classification of Genetic Texts
  name:
    name part: Bolshoy, Alexander.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Volkovich, Zeev (Vladimir).
    role:
      role term: author.
  name:
    name part: Kirzhner, Valery.
    role:
      role term: author.
  name:
    name part: Barzily, Zeev.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: 206 p. 67 illus. online resource.
  abstract: The study of language texts at the level of formal non-semantic models has a long history. Suffice it to say that the well-known Markov chains were first introduced as one of such models. The representation of biological data as text and, consequently, applications of text-analysis models in the field of comparative genomics are substantially newer; nevertheless the methods are well developed. In this book, we try to juxtapose linguistic and bioinformatics models of text analysis. So, it can be read, in a sense, “in two directions” – the book is written so as to appeal to the bioinformatician, who may be interested in finding techniques that had initially appeared in the natural language analysis, and to computational linguist, who may be surprised to discover familiar methods used in bioinformatics. In the presentation of the material, the authors, nevertheless, give preference their professional field - bioinformatics. Therefore, even a specialist in bioinformatics can find something new himself in this book. For example, this book includes a review of the main data mining models generating the text spectra. The chapters of the book assume neither advanced mathematical skills nor beginner knowledge of molecular biology. Relevant biological concepts are introduced in the beginning of the book. Several computer science issues relevant to the topics of the book are reviewed in the three appendices: clustering, sequence complexity, and DNA curvature modeling.
  table of contents: Biological Background -- Biological Classification -- Mathematical Models for the Analysis of Natural-Language Documents -- DNA Texts -- N-Gram Spectra of the DNA Text -- Application of Compositional Spectra to DNA Sequences -- Marker-Function Profile-Based Clustering -- Genome as a Bag of Genes – The Whole-Genome Phylogenetics.
  note: by Alexander Bolshoy, Zeev (Vladimir) Volkovich, Valery Kirzhner, Zeev Barzily.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer-aided engineering
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Computer Science
  subject:
    topic: Computer-Aided Engineering (CAD, CAE) and Design
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  classification: TA345-345.5
  classification: 620.00420285
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 286
  identifier: 9783642129520
  identifier: http://dx.doi.org/10.1007/978-3-642-12952-0
  record info:
    record creation date: 100607
    record change date: 20151204190738.0
    record identifier: 978-3-642-12952-0