000			03369nam a22005175i 4500
001			978-0-387-69765-9
003			DE-He213
005			20250710084010.0
007			cr nn 008mamaa
008			110402s2009 xxu| s |||| 0|eng d
020			_a9780387697659 _a99780387697659
024	7		_a10.1007/978-0-387-69765-9 _2doi
100	1		_aLi, Xiaochun. _eeditor.
245	1	0	_aHigh-Dimensional Data Analysis in Cancer Research _h[recurso electrónico] / _cedited by Xiaochun Li, Ronghui Xu.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_aVIII, 392p. 23 illus., 6 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aApplied Bioinformatics and Biostatistics in Cancer Research
505	0		_aOn the Role and Potential of High-Dimensional Biologic Data in Cancer Research -- Variable selection in regression - estimation, prediction,sparsity, inference -- Multivariate Nonparametric Regression -- Risk Estimation -- Tree-Based Methods -- Support Vector Machine Classification for High Dimensional Microarray Data Analysis, With Applications in Cancer Research -- Bayesian Approaches: Nonparametric Bayesian Analysis of Gene Expression Data.
520			_aWith the advent of high-throughput technologies, various types of high-dimensional data have been generated in recent years for the understanding of biological processes, especially processes that relate to disease occurrence or management of cancer. Motivated by these important applications in cancer research, there has been a dramatic growth in the development of statistical methodology in the analysis of high-dimensional data, particularly related to regression model selection, estimation and prediction. High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics. All the chapters included in this volume contain interesting case studies to demonstrate the analysis methodology. High-Dimensional Data Analysis in Cancer Research is an invaluable reference for researchers, statisticians, bioinformaticians, graduate students and data analysts working in the fields of cancer research.
650		0	_aMEDICINE.
650		0	_aONCOLOGY.
650		0	_aHUMAN GENETICS.
650		0	_aMEDICAL LABORATORIES.
650		0	_aMICROBIOLOGY.
650		0	_aNEUROSCIENCES.
650	1	4	_aBIOMEDICINE.
650	2	4	_aCANCER RESEARCH.
650	2	4	_aLABORATORY MEDICINE.
650	2	4	_aHUMAN GENETICS.
650	2	4	_aMEDICAL MICROBIOLOGY.
650	2	4	_aMOLECULAR MEDICINE.
650	2	4	_aNEUROSCIENCES.
700	1		_aXu, Ronghui. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387697635
830		0	_aApplied Bioinformatics and Biostatistics in Cancer Research
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-69765-9 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SBL
942			_2ddc _cER
999			_c58132 _d58132