Biomedical Informatics for Cancer Research
(Sprache: Englisch)
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.
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Produktinformationen zu „Biomedical Informatics for Cancer Research “
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.
Klappentext zu „Biomedical Informatics for Cancer Research “
This book will review work from a number of researchers who have produced open source software addressing the need for data management, integration, analysis, and visualization to aid cancer research. With the advent of high-throughput technologies in biomedicine, the need for data management and appropriate data analysis tools in genomics has increased dramatically, joining clinical trials data as a major driver of informatics at cancer research centers. The gathering of this data requires careful encoding of metadata, usually through the use of controlled vocabularies or ontologies, as well as the linking of data from model organisms, done at both a physiological level (e.g., anatomy) and at a molecular level (e.g., orthology). This data will then find use within computational and statistical models, which require data pipelines and analysis systems, as well as algorithms, visualization methods, and computational modeling systems. We will introduce open source tools available for these aspects of the problem. The editors plan to divide the book into five sections, beginning with a section containing high level overviews of the field and key issues. This will include an introductory review of informatics in cancer research, followed by five overviews addressing issues in authentication and authorization, data management, data pipelines and annotations, algorithms and models, and the NCI caBIG initiative. This will be followed by sections dedicated to data systems, data pipelines, algorithms for analysis and visualization, and modeling systems. Each of these areas has seen publication of open source tools, ranging from the widely known R/Bioconductor package to little known but powerful systems such as SImmune for biochemical modeling. The area of laboratory information management systems has seen development of a number of unpublished but powerful systems, which we would also include. Three groups have agreed to provide chapters in this area (USC/Norris CAFE
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extensible clinical trials system, St Jude Unified LIMS, Fox Chase/British Columbia flow cytometry LIMS). While there has been a great deal of development of informatics tools that can be applied to problems in cancer research, there has not been adequate dissemination of details on these tools to the community. As such, there remains low adoption of all but a few tools. This book aims to increase overall adoption of tools by providing cancer center leaders and researchers with a single volume detailing both issues that must be addressed and tools that are ready for use. TOC: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.- The caBIG® Clinical Trials Suite.- The CAISIS Research Data System.- A Common Application Framework that is Extensible: CAF--É.- Shared Resource Management (SRM).- The caBIG® Life Science 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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Inhaltsverzeichnis zu „Biomedical Informatics for Cancer Research “
Section 1: Concepts, Issues, and Approaches 1. Biomedical Informatics for Cancer Research: Introduction Michael F. Ochs, John T. Casagrande, Ramana V. Davuluri 2. Clinical Research Systems and Integration with Medical Systems Joyce C. Niland, Layla Rouse 3. Data Management, Databases and Warehousing Waqas Amin, Hyunseok Peter Kang, Michael J. Becich 4. Middleware Architecture Approaches for Collaborative Cancer Research Tahsin Kurc, Ashish Sharma, Scott Oster, Tony Pan, Shannon Hastings, Stephen Langella, David Ervin, Justin Permar, Daniel Brat, TJ Fitzgerald ,James Purdy, Walter Bosch, Joel Saltz 5. Federated Authentication Frank J. Manion,, William Weems, and William McNamee 6. Genomics Data Analysis Pipelines Michael F. Ochs 7. Mathematical Modeling in Cancer Robert A. Gatenby 8. Reproducible Research Concepts and Tools for Cancer Bioinformatics Vincent J Carey, Victoria Stodden 9. The Cancer Biomedical Informatics Grid (caBIG(R)): An Evolving Community for Cancer Research J. Robert Beck Section 2: Tools and Applications 10. The caBIG(R) Clinical Trials Suite John Speakman 11. The CAISIS Research Data System Paul Fearn, Frank Sculli 12. A Common Application Framework that is Extensible: CAF--Ae Richard Evans, Mark DeTomaso, Reed Comire, Vaibhav Bora, Jeet Poonater, Aarti Vaishnav, Scott Catherall, John T. Casagrande 13. Shared Resource Management (SRM) Matt Stine, Vicki Beal, Nilesh Dosooye, Yingliang Du, Rama Gundapaneni, Andrew Pappas, Srinivas Raghavan, Sundeep Shakya, Roshan Shrestha, Momodou Sanyang, Clayton Naeve 14. The caBIG(R) Life Science Distribution Juli Klemm, Anand Basu, Ian Fore, Aris Floratos, George Komatsoulis 15. MeV: MultiExperiment Viewer Eleanor Howe, Kristina Holton, Sarita Nair, Daniel Schlauch, Raktim Sinha and John Quackenbush 16. Authentication and Authorization in Cancer Research Systems Stephen Langella, Shannon Hastings, Scott Oster, Philip Payne, Frank Siebenlist 17. Caching and Visualizing Statistical Analyses Roger D. Peng, Duncan
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Temple Lang 18. Familial Cancer Risk Assessment Using BayesMendel Amanda Blackford, Giovanni Parmigiani 19. Interpreting and Comparing Clustering Experiments through Graph Visualization and Ontology Statistical Enrichment with the ClutrFree Package Ghislain Bidaut 20. Enhanced Dynamic Documents for Reproducible Research Deborah Nolan, Roger D. Peng, Duncan Temple Lang.
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Bibliographische Angaben
- 2010, XVIII, 354 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Herausgegeben: Michael F. Ochs, John T. Cassagrande, Ramana V. Davuluri
- Verlag: Springer, Berlin
- ISBN-10: 144195712X
- ISBN-13: 9781441957122
- Erscheinungsdatum: 01.04.2010
Sprache:
Englisch
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