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Autor(en): 
  • Burkowski Forbes J.
  • Structural Bioinformatics: An Algorithmic Approach 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Oktober 2008  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Biology, life sciences / computer science / COMPUTERS / Computer Science / coordinate transformation algorithms / machine learning in drug design / molecular geometry analysis / protein superposition methods / RNA structure prediction
    ISBN:  9781584886839 
    EAN-Code: 
    9781584886839 
    Verlag:  Taylor and Francis 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Chapman & Hall/CRC Computational Biology Series  
    Dimensionen:  H 229 mm / B 152 mm / D 27 mm 
    Gewicht:  771 gr 
    Seiten:  442 
    Illustration:  schwarz-weiss Illustrationen, farbige Illustrationen, Tabellen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics
    Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.

    Helps Students Go Further in Their Study of Structural Biology
    Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.

    At the Crossroads of Biology, Mathematics, and Computer Science
    Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).

      
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