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020			_a9780387230771 _9978-0-387-23077-1
024	7		_a10.1007/b100565 _2doi
035			_avtls000329846
039		9	_a201509031106 _bVLOAD _c201405070451 _dVLOAD _c201401311320 _dstaff _c201401311145 _dstaff _y201401291441 _zstaff _wmsplit0.mrc _x268
050		4	_aRB155-155.8
100	1		_aShoemaker, Jennifer S. _eeditor. _9299983
245	1	0	_aMethods of Microarray Data Analysis / _cedited by Jennifer S. Shoemaker, Simon M. Lin.
250			_aIV.
264		1	_aBoston, MA : _bSpringer US, _c2005.
300			_aXVI, 256 páginas, _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aCancer: Clinical Challenges and Opportunities -- Gene Expression Data and Survival Analysis -- The Needed Replicates of Arrays in Microarray Experiments for Reliable Statistical Evaluation -- Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer -- Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies -- Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments -- Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expression and Survival -- Associating Microarray Data with a Survival Endpoint -- Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas -- Probabilistic Lung Cancer Models Conditioned on Gene Expression Microarray Data -- Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes -- Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer -- Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering -- Higher Dimensional Approach for Classification of Lung Cancer Microarray Data -- Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods -- A Combinatorial Approach to the Analysis of Differential Gene Expression Data -- Genes Associated with Prognosis in Adenocarcinoma Across Studies at Multiple Institutions.
520			_aAs studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. METHODS OF MICROARRAY DATA ANALYSIS IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III). In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state-of-the-art of microarray data analysis. Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aLin, Simon M. _eeditor. _9299984
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387230740
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b100565 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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