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: Artificial intelligence methods and tools for systems biology
  name:
    name part: Dubitzky, Werner
    name part: 1958-
  name:
    name part: Azuaje, Francisco.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvii, 221 pages) : illustrations
  abstract: This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain. As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.
  table of contents: Cover -- Contents -- Preface -- Lazy Learning for Predictive Toxicology based on a Chemical Ontology -- QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds -- Characterizing Gene Expression Time Series using a Hidden Markov Model -- Analysis of Large-Scale mRNA Expression Data Sets by Genetic Algorithms -- A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data -- Cooperative Metaheuristics for Exploring Proteomic Data -- Integrating Gene Expression Data, Protein Interaction Data, and Ontology-Based Literature Searches -- Ontologies in Bioinformatics and Systems Biology -- Natural Language Processing and Systems Biology -- Systems Level Modeling of Gene Regulatory Networks -- Computational Neuroscience for Cognitive Brain Functions -- Index -- Last Page.
  note: edited by Werner Dubitzky and Francisco Azuaje.
  note: Electronic resource.
  note: Includes bibliographical references and index.
  subject:
    topic: Artificial intelligence
    topic: Biological applications
  subject:
    topic: Biology
    genre: Classification
    topic: Data processing
  subject:
    topic: Biological systems
    topic: Research
    topic: Methodology
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Computational Biology
  subject:
    topic: Computer science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Evolutionary Biology
  subject:
    topic: Life Sciences, general
  subject:
    topic: Computer Science, general
  subject:
    topic: Intelligence artificielle
    topic: Applications biologiques
  subject:
    topic: Biologie
    genre: Classification
    topic: Informatique
  subject:
    topic: Systèmes biologiques
    topic: Recherche
    topic: Méthodologie
  subject:
    topic: NATURE
    topic: Reference
  subject:
    topic: SCIENCE
    topic: Life Sciences
    topic: General
  subject:
    topic: SCIENCE
    topic: Life Sciences
    topic: Biology
  classification: QH324.25 .A78 2004eb
  classification: 570.285
  classification: 2005 N-670
  classification: QU 26.5 A791 2004
  related item:
    title info:
      title: Artificial intelligence methods and tools for systems biology
    origin info:
      publisher: Dordrecht ; Norwell, MA : Springer, c2004
    identifier: (OCoLC)58421928
  related item:
    title info:
      title: Computational biology ; v. 5
  identifier: 1402028652 (electronic bk.)
  identifier: 9781402028656 (electronic bk.)
  identifier: 9781402028595
  identifier: 1402028598
  identifier: 1402029594
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 061114
    record change date: 20260422214827.1
    record identifier: in00003508339
    language of cataloging:
      language term: eng