Gespeichert in:
| Weitere beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2006
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1017/CBO9780511584589 https://doi.org/10.1017/CBO9780511584589 https://doi.org/10.1017/CBO9780511584589 |
| Zusammenfassung: | The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Umfang: | 1 online resource (xviii, 437 pages) |
| ISBN: | 9780511584589 |
| DOI: | 10.1017/CBO9780511584589 |
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| id | DE-604.BV043942582 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:49:19Z |
| institution | BVB |
| isbn | 9780511584589 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029351552 |
| oclc_num | 967602236 |
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| owner_facet | DE-12 DE-92 |
| physical | 1 online resource (xviii, 437 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2006 |
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| publisher | Cambridge University Press |
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| spelling | Bayesian inference for gene expression and proteomics edited by Kim-Anh Do, Peter Müller, Marina Vannucci Bayesian Inference for Gene Expression & Proteomics Cambridge Cambridge University Press 2006 1 online resource (xviii, 437 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions Gene expression / Statistical methods Proteomics / Statistical methods Genexpression (DE-588)4020136-3 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Proteomanalyse (DE-588)4596545-6 gnd rswk-swf Proteomanalyse (DE-588)4596545-6 s Datenanalyse (DE-588)4123037-1 s 1\p DE-604 Genexpression (DE-588)4020136-3 s 2\p DE-604 Do, Kim-Anh 1960- edt Müller, Peter 1963 August 9- edt Vannucci, Marina 1966- edt Erscheint auch als Druckausgabe 978-0-521-86092-5 Erscheint auch als Druckausgabe 978-1-107-63698-9 https://doi.org/10.1017/CBO9780511584589 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bayesian inference for gene expression and proteomics Gene expression / Statistical methods Proteomics / Statistical methods Genexpression (DE-588)4020136-3 gnd Datenanalyse (DE-588)4123037-1 gnd Proteomanalyse (DE-588)4596545-6 gnd |
| subject_GND | (DE-588)4020136-3 (DE-588)4123037-1 (DE-588)4596545-6 |
| title | Bayesian inference for gene expression and proteomics |
| title_alt | Bayesian Inference for Gene Expression & Proteomics |
| title_auth | Bayesian inference for gene expression and proteomics |
| title_exact_search | Bayesian inference for gene expression and proteomics |
| title_full | Bayesian inference for gene expression and proteomics edited by Kim-Anh Do, Peter Müller, Marina Vannucci |
| title_fullStr | Bayesian inference for gene expression and proteomics edited by Kim-Anh Do, Peter Müller, Marina Vannucci |
| title_full_unstemmed | Bayesian inference for gene expression and proteomics edited by Kim-Anh Do, Peter Müller, Marina Vannucci |
| title_short | Bayesian inference for gene expression and proteomics |
| title_sort | bayesian inference for gene expression and proteomics |
| topic | Gene expression / Statistical methods Proteomics / Statistical methods Genexpression (DE-588)4020136-3 gnd Datenanalyse (DE-588)4123037-1 gnd Proteomanalyse (DE-588)4596545-6 gnd |
| topic_facet | Gene expression / Statistical methods Proteomics / Statistical methods Genexpression Datenanalyse Proteomanalyse |
| url | https://doi.org/10.1017/CBO9780511584589 |
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