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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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 “
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.
Inhaltsverzeichnis zu „Biomedical Informatics for Cancer Research “
Section 1: Concepts, Issues, and Approaches1. 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®): An Evolving Community for Cancer Research
J. Robert Beck
Section 2: Tools and Applications
10. The caBIG® Clinical Trials Suite
John Speakman
11. The CAISIS Research Data System
Paul Fearn, Frank Sculli
12. A Common Application Framework that is Extensible: CAF--É
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® 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
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Analyses
Roger D. Peng, Duncan 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
Roger D. Peng, Duncan 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
... weniger
Bibliographische Angaben
- 2014, 2010, XVIII, 354 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Michael F. Ochs, John T. Casagrande, Ramana V. Davuluri
- Verlag: Springer, Berlin
- ISBN-10: 1489984518
- ISBN-13: 9781489984517
Sprache:
Englisch
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