Computational Biology [recurso electrónico] : Issues and Applications in Oncology / edited by Tuan Pham.

Por: Pham, Tuan [editor.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Applied Bioinformatics and Biostatistics in Cancer ResearchEditor: New York, NY : Springer New York, 2010Descripción: VIII, 309p. 90 illus., 26 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441908117Tema(s): Medicine | Oncology | Toxicology | Biomedicine | Cancer Research | Pharmacology/ToxicologyFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 614.5999 Clasificación LoC:RC261-271Recursos en línea: Libro electrónicoTexto
Contenidos:
Identification of Relevant Genes from Microarray Experiments based on Partial Least Squares Weights: Application to Cancer Genomics -- Geometric Biclustering and Its Applications to Cancer Tissue Classification Based on DNA Microarray Gene Expression Data -- Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures -- Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling -- Multicluster Class-Based Classification for the Diagnosis of Suspicious Areas in Digital Mammograms -- Analysis of Cancer Data Using Evolutionary Computation -- Analysis of Population-Based Genetic Association Studies Applied to Cancer Susceptibility and Prognosis -- Selected Applications of Graph-Based Tracking Methods for Cancer Research -- Recent Advances in Cell Classification for Cancer Research and Drug Discovery -- Computational Tools and Resources for Systems Biology Approaches in Cancer -- Laser Speckle Imaging for Blood Flow Analysis -- The Challenges in Blood Proteomic Biomarker Discovery.
En: Springer eBooksResumen: Computational Biology: Issues and Applications in Oncology provides a comprehensive report on recent techniques and results in computational oncology essential to the knowledge of scientists, engineers, as well as postgraduate students working on the areas of computational biology, bioinformatics, and medical informatics. With chapters timely prepared and written by experts in the field, this in-depth and up-to-date volume covers advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, image and pattern analysis applied to cancer research. The literature and coverage of a spectrum of key topics in issues and applications in oncology make this a useful resource to computational life-science researchers wishing to enhance the most recent knowledge to facilitate their own investigations.
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Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos RC261 -271 (Browse shelf(Abre debajo)) 1 No para préstamo 371185-2001

Identification of Relevant Genes from Microarray Experiments based on Partial Least Squares Weights: Application to Cancer Genomics -- Geometric Biclustering and Its Applications to Cancer Tissue Classification Based on DNA Microarray Gene Expression Data -- Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures -- Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling -- Multicluster Class-Based Classification for the Diagnosis of Suspicious Areas in Digital Mammograms -- Analysis of Cancer Data Using Evolutionary Computation -- Analysis of Population-Based Genetic Association Studies Applied to Cancer Susceptibility and Prognosis -- Selected Applications of Graph-Based Tracking Methods for Cancer Research -- Recent Advances in Cell Classification for Cancer Research and Drug Discovery -- Computational Tools and Resources for Systems Biology Approaches in Cancer -- Laser Speckle Imaging for Blood Flow Analysis -- The Challenges in Blood Proteomic Biomarker Discovery.

Computational Biology: Issues and Applications in Oncology provides a comprehensive report on recent techniques and results in computational oncology essential to the knowledge of scientists, engineers, as well as postgraduate students working on the areas of computational biology, bioinformatics, and medical informatics. With chapters timely prepared and written by experts in the field, this in-depth and up-to-date volume covers advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, image and pattern analysis applied to cancer research. The literature and coverage of a spectrum of key topics in issues and applications in oncology make this a useful resource to computational life-science researchers wishing to enhance the most recent knowledge to facilitate their own investigations.

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