000			03702nam a22004095i 4500
999			_c391314 _d391314
001			391314
003			ES-MaUEC
005			20230511113826.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			100301s2002 xxu| s |||| 0|eng d
020			_a9781592592425
024	7		_a10.1385/1592592422 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH323.5 _b2002 EB
245	0	0	_aBiostatistical Methods _cedited by Stephen W. Looney
250			_a1st edition 2002
264		1	_aTotowa, NJ _bHumana Press _c2002
300			_a1 recurso en línea (XII, 216 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v184
505	0		_aStatistical Contributions to Molecular Biology -- Linking Image Quantitation and Data Analysis -- to Microarray Experimentation and Analysis -- Statistical Methods for Proteomics -- Statistical Methods for Assessing Biomarkers -- Power and Sample Size Considerations in Molecular Biology -- Models for Determining Genetic Susceptibility and Predicting Outcome -- Multiple Tests for Genetic Effects in Association Studies -- Statistical Considerations in Assessing Molecular Markers for Cancer Prognosis and Treatment Efficacy -- Power of the Rank Test for Multi-Strata Case-Control Studies with Ordinal Exposure Variables.
520			_aThe use of biostatistical techniques in molecular biology has grown tremendously in recent years and is now essential for the correct interpretation of a wide variety of laboratory studies. In Biostatistical Methods, a panel of leading biostatisticians and biomedical researchers describe all the key techniques used to solve commonly occurring analytical problems in molecular biology, and demonstrate how these methods can identify new markers for exposure to a risk factor, or for determining disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, determining new disease biomarkers, and designing studies with adequate levels of statistical power. In the case of genetic effects in human populations, the authors describe sophisticated statistical methods to control the overall false-positive rate when many statistical tests are used in linking particular alleles to the occurrence of disease. Other methods discussed are those used to validate statistical approaches for analyzing the E-D association, to study the associations between disease and the inheritance of particular genetic variants, and to examine real data sets. There are also useful recommendations for statistical and data management software (JAVA, Oracle, S-Plus, STATA, and SAS) . Accessible, state-of-the-art, and highly practical, Biostatistical Methods provides an excellent starting point both for statisticians just beginning work on problems in molecular biology, and for all molecular biologists who want to use biostatistics in genetics research designed to uncover the causes and treatments of disease.
988			_aSpringer_Protocols_2002
650		7	_2embne _9139105 _aBiometría
650		7	_2embne _9139103 _aBiología molecular
776	0	8	_iPrinted edition: _z9781617372711
776	0	8	_iPrinted edition: _z9780896039513
776	0	8	_iPrinted edition: _z9781489938916
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1385/1592592422 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eu _zSI