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Herausgeber: 
  • Simon M. Lin
  • Kimberly F. Johnson
  • Methods of Microarray Data Analysis: Papers from CAMDA ¿00 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  November 2001  
    Genre:  Naturwissensch., Medizin, Technik 
     
    B / biochemistry / Biochemistry, general / Biomedical and Life Sciences / Biostatistics / Epidemiology & medical statistics / Human Genetics / Medical Genetics / Probability & statistics / Statistics / Statistics for Life Sciences, Medicine, Health Sciences
    ISBN:  9780792375647 
    EAN-Code: 
    9780792375647 
    Verlag:  Springer Us 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 18 mm 
    Gewicht:  521 gr 
    Seiten:  208 
    Zus. Info:  HC runder Rücken kaschiert 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods ranging from data normalization, feature selection and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.

      
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