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Bioinformatics and computational biology solutions using R and Bioconductor : with 128 illustrations / Robert Gentleman... [et al.].

Contributor(s): Gentleman, Robert, 1959- [ed.] | Carey, Vincent J [ed.] | Huber, Wolfgang [ed.] | Irizarry, Rafael A [ed.] | Dudoit, Sandrine [ed.].
Material type: TextTextSeries: Statistics for biology and health. Springer. Publisher: New York : Springer, 2005Description: xix, 473 p. : col. ill., charts ; 25 cm.ISBN: 9780387251462; 0387251464.Subject(s): Bioconductor (Computer file) | Bioinformatics | Computational biology -- Methods | Genetic techniques | Genomics | R (Computer program language) | Perrotis College -- Master of science degreeDDC classification: 570.285
Partial contents:
I. Preprocessing data from genomic experiments : 1. Preprocessing overview. - 2. Preprocessing high-density oligonucleotide arrays. - 3. Quality assessment of affymetrix GeneChip data. - 4. Preprocessing two-color spotted arrays. - 5. Cell-based assays. - 6. SELDI-TOF mass spectrometry protein data.
II. Meta-data: Biological Annotation and Visualization : 7. Meta-data resources and tools in Bioconductor. - 8. Querying on-line resources. - 9. Interactive outputs. - 10. Visualizing data.
III. Statistical Analysis for Genomic Experiments : 11. Analysis overview. - 12. Distance measures in DNA microarray data analysis. - 13. Cluster analysis of genomic data. - 14. Analysis of differential gene expression studies. - 15. Multiple testing procedures: the multtest Package and applications to genomics. - 16. Machine learning concepts and tools for statistical genomics. - 17. Ensemble methods of computational inference. - 18. Browser-based affymetrix analysis and annotation.
IV. Graphs and Networks : 19. Introduction and motivating examples. - 20. Graphs. - 21. Bioconductor software for graphs. - 22. Case studies using graphs on biological data.
V. Case Studies : 23. limma: Linear Models for Microarray Data. - 24. Classification with gene expression data. - 25. From CEL files to annotated lists of interesting genes. - Appendix A: Details on selected resources.
Summary: Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
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Book Non Fiction Book Non Fiction "George and Charlotte Draper" Information & Media Hub

Παράρτημα της Κεντρικής Βιβλιοθήκης στο Κολέγιο Περρωτής.

"George and Charlotte Draper" Information & Media Hub

Παράρτημα της Κεντρικής Βιβλιοθήκης στο Κολέγιο Περρωτής.

Perrotis College - Master degree 570.285 Bio (Browse shelf) Available Graduate (Research Methods and Data Management - level 7) -- Recommended 13019578

Hard cover.

Includes index.

Bibliography : p. [445]-463.

I. Preprocessing data from genomic experiments : 1. Preprocessing overview. - 2. Preprocessing high-density oligonucleotide arrays. - 3. Quality assessment of affymetrix GeneChip data. - 4. Preprocessing two-color spotted arrays. - 5. Cell-based assays. - 6. SELDI-TOF mass spectrometry protein data.

II. Meta-data: Biological Annotation and Visualization : 7. Meta-data resources and tools in Bioconductor. - 8. Querying on-line resources. - 9. Interactive outputs. - 10. Visualizing data.

III. Statistical Analysis for Genomic Experiments : 11. Analysis overview. - 12. Distance measures in DNA microarray data analysis. - 13. Cluster analysis of genomic data. - 14. Analysis of differential gene expression studies. - 15. Multiple testing procedures: the multtest Package and applications to genomics. - 16. Machine learning concepts and tools for statistical genomics. - 17. Ensemble methods of computational inference. - 18. Browser-based affymetrix analysis and annotation.

IV. Graphs and Networks : 19. Introduction and motivating examples. - 20. Graphs. - 21. Bioconductor software for graphs. - 22. Case studies using graphs on biological data.

V. Case Studies : 23. limma: Linear Models for Microarray Data. - 24. Classification with gene expression data. - 25. From CEL files to annotated lists of interesting genes. - Appendix A: Details on selected resources.

Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

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