國立虎尾科技大學 |

Bayesian inference for gene expression and proteomics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian inference for gene expression and proteomics // edited by Kim-Anh Do, Peter Müller, Marina Vannucci.
其他題名:	Bayesian Inference for Gene Expression & Proteomics
其他作者:	Do, Kim-Anh,
面頁冊數:	1 online resource (xviii, 437 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Gene expression - Statistical methods. -
電子資源:	https://doi.org/10.1017/CBO9780511584589
ISBN:	9780511584589 (ebook)

Bayesian inference for gene expression and proteomics /
Bayesian inference for gene expression and proteomics /Bayesian Inference for Gene Expression & Proteomicsedited by Kim-Anh Do, Peter Müller, Marina Vannucci. - 1 online resource (xviii, 437 pages) :digital, PDF file(s).

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.

ISBN: 9780511584589 (ebook)Subjects--Topical Terms:

799704
Gene expression
--Statistical methods.

LC Class. No.: QH450 / .B39 2006

Dewey Class. No.: 572.8/6501519542

Bayesian inference for gene expression and proteomics /
LDR:02190nam a2200301 i 4500 001 1124242
003 UkCbUP
005 20160204124423.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2006||||enk o ||1 0|eng|d
020 $a 9780511584589 (ebook)
020 $z 9780521860925 (hardback)
020 $z 9781107636989 (paperback)
035 $a CR9780511584589
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a QH450 $b .B39 2006
082 0 0 $a 572.8/6501519542 $2 22
245 0 0 $a Bayesian inference for gene expression and proteomics / $c edited by Kim-Anh Do, Peter Müller, Marina Vannucci.
246 3 $a Bayesian Inference for Gene Expression & Proteomics
264 1 $a Cambridge : $b Cambridge University Press, $c 2006.
300 $a 1 online resource (xviii, 437 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520 $a 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.
650 0 $a Gene expression $x Statistical methods. $3 799704
650 0 $a Proteomics $x Statistical methods. $3 799705
700 1 $a Do, Kim-Anh, $d 1960- $e editor. $3 1441641
700 1 $a Müller, Peter, $d 1963 August 9- $e editor. $3 1441642
700 1 $a Vannucci, Marina, $d 1966- $e editor. $3 1441643
776 0 8 $i Print version: $z 9780521860925
856 4 0 $u https://doi.org/10.1017/CBO9780511584589