Bayesian Inference Gene Expression (22 results)

Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condition: Very Good. In stock ready to dispatch from the UK.

Hardcover. Condition: Very Good. Bayesian Inference for Gene Expression and Proteomics This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

Hardcover. Condition: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Paperback. Condition: Like New. First Edition. Firm, square and tight, just showing a few minor rubs. Hence a non-text page is stamped 'damaged'. Despite such this book looks and feels unread and is actually in nearly new condition. Thus the contents are crisp and fresh. Now offered for sale at a special bargain price.

Condition: New.

Condition: New. In.

Paperback. Condition: New.

Condition: New. Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. Editor(s): Do, Kim-Anh; Muller, Peter; Vannucci, Marina. Num Pages: 456 pages, 22 tables. BIC Classification: PBT; PSAK. Category: (P) Professional & Vocational. Dimension: 220 x 140 x 25. Weight in Grams: 572. . 2012. Paperback. . . . .

paperback. Condition: Gut. 468 Seiten; 9781107636989.3 Gewicht in Gramm: 1.

Condition: New. pp. 456.

Condition: New. Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. Editor(s): Do, Kim-Anh; Muller, Peter; Vannucci, Marina. Num Pages: 456 pages, 22 tables. BIC Classification: PBT; PSAK. Category: (P) Professional & Vocational. Dimension: 220 x 140 x 25. Weight in Grams: 572. . 2012. Paperback. . . . . Books ship from the US and Ireland.

Paperback. Condition: Brand New. 1st edition. 456 pages. 8.58x5.51x1.02 inches. In Stock.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Paperback. Condition: new. Paperback. 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. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: Brand New. 1st edition. 456 pages. 8.58x5.51x1.02 inches. In Stock. This item is printed on demand.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Print on Demand pp. 456.

Condition: New. PRINT ON DEMAND pp. 456.

Paperback. Condition: new. Paperback. 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. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and cluste.

Paperback. Condition: new. Paperback. 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. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.