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Biomedical Informatics for Cancer Research 2010 ed. [Hardback]

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  • Formāts: Hardback, 354 pages, height x width: 235x155 mm, weight: 791 g, XVIII, 354 p., 1 Hardback
  • Izdošanas datums: 01-Apr-2010
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 144195712X
  • ISBN-13: 9781441957122
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  • Formāts: Hardback, 354 pages, height x width: 235x155 mm, weight: 791 g, XVIII, 354 p., 1 Hardback
  • Izdošanas datums: 01-Apr-2010
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 144195712X
  • ISBN-13: 9781441957122
Citas grāmatas par šo tēmu:
This is an overview of software produced to aid cancer research. It first reviews informatics in cancer research then covers authentication and authorization, data management, data pipelines and annotations, algorithms and models and the NCI caBIG initiative.

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.
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.