Biomedical Informatics for Cancer Research

Herausgegeben:Ochs, Michael F.; Casagrande, John T.; Davuluri, Ramana V.

• Broschiertes Buch

Produktbeschreibung

view, showing that multiple molecular pathways must be affected for cancer to develop, but with different specific proteins in each pathway mutated or differentially expressed in a given tumor (The Cancer Genome Atlas Research Network 2008; Parsons et al. 2008). Different studies demonstrated that while widespread mutations exist in cancer, not all mutations drive cancer development (Lin et al. 2007). This suggests a need to target only a deleterious subset of aberrant proteins, since any tre- ment must aim to improve health to justify its potential side effects. Treatment for cancer must become highly individualized, focusing on the specific aberrant driver proteins in an individual. This drives a need for informatics in cancer far beyond the need in other diseases. For instance, routine treatment with statins has become widespread for minimizing heart disease, with most patients responding to standard doses (Wilt et al. 2004). In contrast, standard treatment for cancer must become tailored to the molecular phenotype of an individual tumor, with each patient receiving a different combination of therapeutics aimed at the specific aberrant proteins driving the cancer. Tracking the aberrations that drive cancers, identifying biomarkers unique to each individual for molecular-level di- nosis and treatment response, monitoring adverse events and complex dosing schedules, and providing annotated molecular data for ongoing research to improve treatments comprise a major biomedical informatics need.

Produktdetails

• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4899-8451-7
• 2010
• Seitenzahl: 372
• Erscheinungstermin: 26. November 2014
• Englisch
• Abmessung: 235mm x 155mm x 21mm
• Gewicht: 569g
• ISBN-13: 9781489984517
• ISBN-10: 1489984518
• Artikelnr.: 41900702

Inhaltsangabe

Concepts, Issues, and Approaches.- Biomedical Informatics for Cancer Research: Introduction.- Clinical Research Systems and Integration with Medical Systems.- Data Management, Databases, and Warehousing.- Middleware Architecture Approaches for Collaborative Cancer Research.- Federated Authentication.- Genomics Data Analysis Pipelines.- Mathematical Modeling in Cancer.- Reproducible Research Concepts and Tools for Cancer Bioinformatics.- The Cancer Biomedical Informatics Grid (caBIG'): An Evolving Community for Cancer Research.- Tools and Applications.- The caBIG' Clinical Trials Suite.- The CAISIS Research Data System.- A Common Application Framework that is Extensible: CAF-É.- Shared Resource Management.- The caBIG® Life Sciences Distribution.- MeV: MultiExperiment Viewer.- Authentication and Authorization in Cancer Research Systems.- Caching and Visualizing Statistical Analyses.- Familial Cancer Risk Assessment Using BayesMendel.- Interpreting and Comparing Clustering Experiments Through Graph Visualization and Ontology Statistical Enrichment with the ClutrFree Package.- Enhanced Dynamic Documents for Reproducible Research.

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Concepts, Issues, and Approaches.- Biomedical Informatics for Cancer Research: Introduction.- Clinical Research Systems and Integration with Medical Systems.- Data Management, Databases, and Warehousing.- Middleware Architecture Approaches for Collaborative Cancer Research.- Federated Authentication.- Genomics Data Analysis Pipelines.- Mathematical Modeling in Cancer.- Reproducible Research Concepts and Tools for Cancer Bioinformatics.- The Cancer Biomedical Informatics Grid (caBIG‚): An Evolving Community for Cancer Research.- Tools and Applications.- The caBIG‚ Clinical Trials Suite.- The CAISIS Research Data System.- A Common Application Framework that is Extensible: CAF-É.- Shared Resource Management.- The caBIG® Life Sciences Distribution.- MeV: MultiExperiment Viewer.- Authentication and Authorization in Cancer Research Systems.- Caching and Visualizing Statistical Analyses.- Familial Cancer Risk Assessment Using BayesMendel.- Interpreting and Comparing Clustering Experiments Through Graph Visualization and Ontology Statistical Enrichment with the ClutrFree Package.- Enhanced Dynamic Documents for Reproducible Research.