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: Introduction to Data Mining for the Life Sciences
  name:
    name part: Sullivan, Rob.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 638 p. online resource.
  abstract: One of the major challenges for the scientific community, a challenge that has been seen in many business disciplines, is the exponential increase in data being generated by new experimental techniques and research. A single microarray experiment, for example, can generate thousands of data points that need to be analyzed, and this problem is predicted to increase. As new techniques in areas such as genomics and proteomics continue to be adopted into the mainstream as the costs fall, the need for effective mechanisms for synthesizing these disparate forms of data together for analysis is of paramount importance. But the sheer volume of data means that traditional techniques need to be augmented by approaches that elicit knowledge from the data, using automated procedures. Data mining provides a set of such techniques, new techniques to integrate, synthesize, and analyze the data, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
  table of contents: Introduction -- Fundamental Concepts -- Data Architecture and Data Modeling -- Representing Data Mining Results -- The Input Side of the Equation -- Statistical Methods -- Bayesian Statistics -- Machine Learning Techniques -- Classification and Prediction -- Informatics -- Systems Biology -- Let’s Call it a Day -- Appendix A -- Appendix B -- Appendix C. Appendix D -- Index.
  note: by Rob Sullivan.
  subject:
    topic: Life sciences
  subject:
    topic: Bioinformatics
  subject:
    topic: Life Sciences
  subject:
    topic: Bioinformatics
  classification: QH324.2-324.25
  classification: 570.285
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781597452908
  identifier: http://dx.doi.org/10.1007/978-1-59745-290-8
  record info:
    record creation date: 120106
    record change date: 20140302021805.0
    record identifier: 978-1-59745-290-8