TEST - Catálogo BURRF
   

Computational Cancer Biology : An Interaction Network Approach / by Mathukumalli Vidyasagar.

Por: Colaborador(es): Tipo de material: TextoTextoSeries SpringerBriefs in Electrical and Computer EngineeringEditor: London : Springer London : Imprint: Springer, 2012Descripción: xii, 80 páginas 11 ilustraciones en color. recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9781447147510
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QH324.2-324.25
Recursos en línea:
Contenidos:
Introduction -- Inferring Genetic Regulatory Networks -- Context-specific Genomic Networks -- Analyzing Statistical Significance -- Separating Drivers from Passengers -- Some Research Directions.
Resumen: This brief introduces readers to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics, building on only a basic background in these two topics.   Aside from providing a self-contained introduction to several aspects of basic biology and to cancer, as well as to the techniques from statistics most commonly used in cancer biology, the brief describes several methods for inferring gene interaction networks from expression data, including one that is reported for the first time in the brief.  The application of these methods is illustrated on actual data from cancer cell lines.  Some promising directions for new research are also discussed.   After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.
Valoración
    Valoración media: 0.0 (0 votos)
No hay ítems correspondientes a este registro

Springer eBooks

Introduction -- Inferring Genetic Regulatory Networks -- Context-specific Genomic Networks -- Analyzing Statistical Significance -- Separating Drivers from Passengers -- Some Research Directions.

This brief introduces readers to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics, building on only a basic background in these two topics.   Aside from providing a self-contained introduction to several aspects of basic biology and to cancer, as well as to the techniques from statistics most commonly used in cancer biology, the brief describes several methods for inferring gene interaction networks from expression data, including one that is reported for the first time in the brief.  The application of these methods is illustrated on actual data from cancer cell lines.  Some promising directions for new research are also discussed.   After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.

Para consulta fuera de la UANL se requiere clave de acceso remoto.

Universidad Autónoma de Nuevo León
Secretaría de Extensión y Cultura - Dirección de Bibliotecas @
Soportado en Koha