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: Hierarchical Feature Selection for Knowledge Discovery
    sub title: Application of Data Mining to the Biology of Ageing
  name:
    name part: Wan, Cen.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 120 p. 52 illus., 23 illus. in color. online resource.
  abstract: This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.
  table of contents: Introduction -- Data Mining Tasks and Paradigms -- Feature Selection Paradigms -- Background on Biology of Ageing and Bioinformatics -- Lazy Hierarchical Feature Selection -- Eager Hierarchical Feature Selection -- Comparison of Lazy and Eager Hierarchical Feature Selection Methods and Biological Interpretation on Frequently Selected Gene Ontology Terms Relevant to the Biology of Ageing -- Conclusions and Research Directions.
  note: by Cen Wan.
  subject:
    topic: Data mining
  subject:
    topic: Bioinformatics
  subject:
    topic: Systems biology
  subject:
    topic: Artificial intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computational Biology/Bioinformatics
  subject:
    topic: Systems Biology
  subject:
    topic: Artificial Intelligence
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advanced Information and Knowledge Processing
  identifier: 9783319979199
  identifier: https://doi.org/10.1007/978-3-319-97919-9
  record info:
    record creation date: 181129
    record change date: 20191220130319.0
    record identifier: 978-3-319-97919-9