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020 _a9783319747750
_9978-3-319-74775-0
024 7 _a10.1007/978-3-319-74775-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
100 1 _aJana, Nanda Dulal.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_937049
245 1 2 _aA Metaheuristic Approach to Protein Structure Prediction
_h[electronic resource] :
_bAlgorithms and Insights from Fitness Landscape Analysis /
_cby Nanda Dulal Jana, Swagatam Das, Jaya Sil.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXXIX, 220 p. 59 illus., 54 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aEmergence, Complexity and Computation,
_x2194-7295 ;
_v31
505 0 _aMetaheuristic Protein Structure Prediction-An Overview -- Related Works -- Continuous Landscape Analysis using Random Walk Algorithm -- Landscape Characterization and Algorithms Selection for the PSP Problem -- The Levy distributed Parameter Adaptive Metaheuristic Algorithm for Protein Structure Prediction -- Protein Structure Prediction using Improved Variants of Metaheuristic Algorithms -- Hybrid Metaheuristic Approach for Protein Structure Prediction -- Conclusions and Future Research.
520 _aThis 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.
650 0 _aComputational intelligence.
_97716
650 0 _aDynamics.
_937050
650 0 _aNonlinear theories.
_93339
650 0 _aArtificial intelligence.
_93407
650 0 _aProteins.
_937051
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aApplied Dynamical Systems.
_932005
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aProteins.
_937051
700 1 _aDas, Swagatam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_937052
700 1 _aSil, Jaya.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_937053
710 2 _aSpringerLink (Online service)
_937054
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319747743
776 0 8 _iPrinted edition:
_z9783319747767
776 0 8 _iPrinted edition:
_z9783030090753
830 0 _aEmergence, Complexity and Computation,
_x2194-7295 ;
_v31
_937055
856 4 0 _uhttps://doi.org/10.1007/978-3-319-74775-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76094
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