Bayesian Analysis of Gene Expression Data - Rilegato

Mallick, Bani K.; Gold, David Lee; Baladandayuthapani, Veera

 
9780470517666: Bayesian Analysis of Gene Expression Data
Rilegato
ISBN 10: 0470517662 ISBN 13: 9780470517666
Casa editrice: John Wiley & Sons Inc, 2009

Sinossi

The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable.

This book:

  • Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data.
  • Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications.
  • Accompanied by website featuring datasets, exercises and solutions.

Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Bani Mallick, Department of Statistics, Texas A&M University, USA.

Veera Balandandayuthapani, Department of Biostatistics, Anderson Cancer Center, Texas, USA.

David L. Gold, Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, The State University of New York, USA.

Dalla quarta di copertina

The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable.

This book:

  • Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data.
  • Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications.
  • Accompanied by website featuring datasets, exercises and solutions.

Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

Dal risvolto di copertina interno

The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable.
 
This book:
* Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data.
* Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications.
* Accompanied by website featuring datasets, exercises and solutions.
 
Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
John Wiley & Sons Inc
Data di pubblicazione
2009
Lingua
Inglese
ISBN 10 
0470517662
ISBN 13 
9780470517666
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
240
Contatto del produttore
non disponibile
Persona responsabile
non disponibile