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Autor(en): 
  • Swagatam Das
  • Jaya Sil
  • Nanda Dulal Jana
  • A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2018  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Applied Dynamical Systems / Artificial Intelligence / B / biochemistry / complexity / Computational complexity / Computational Intelligence / Cybernetics & systems theory / engineering / Life Sciences / Protein Structure / proteins
    ISBN:  9783030090753 
    EAN-Code: 
    9783030090753 
    Verlag:  Springer Nature EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #31 - Emergence, Complexity and Computation  
    Dimensionen:  H 235 mm / B 155 mm / D 13 mm 
    Gewicht:  391 gr 
    Seiten:  220 
    Illustration:  XXIX, 220 p. 59 illus., 54 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  Previously published in hardcover 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past.

     Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction.

     This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.

      



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