LEADER 03952nam  22006135  450 
001 9910299957003321
005 20250609110929.0
010   $a3-319-74775-4
024 7 $a10.1007/978-3-319-74775-0
035   $a(CKB)4100000002892353
035   $a(MiAaPQ)EBC5439422
035   $a(DE-He213)978-3-319-74775-0
035   $a(PPN)225550393
035   $a(MiAaPQ)EBC5591125
035   $a(EXLCZ)994100000002892353
100   $a20180305d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 12$aA Metaheuristic Approach to Protein Structure Prediction $eAlgorithms and Insights from Fitness Landscape Analysis /$fby Nanda Dulal Jana, Swagatam Das, Jaya Sil
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (xxix, 220 pages) $cillustrations
225 1 $aEmergence, Complexity and Computation,$x2194-7287 ;$v31
311 08$a3-319-74774-6 
327   $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.
330   $aThis book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past. Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction. This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.
410  0$aEmergence, Complexity and Computation,$x2194-7287 ;$v31
606   $aComputational intelligence
606   $aComputational complexity
606   $aArtificial intelligence
606   $aProteins
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aProtein Structure$3https://scigraph.springernature.com/ontologies/product-market-codes/L14050
615  0$aComputational intelligence.
615  0$aComputational complexity.
615  0$aArtificial intelligence.
615  0$aProteins.
615 14$aComputational Intelligence.
615 24$aComplexity.
615 24$aArtificial Intelligence.
615 24$aProtein Structure.
676   $a006.3
700   $aJana$b Nanda Dulal$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063364
702   $aDas$b Swagatam$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aSil$b Jaya$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299957003321
996   $aA Metaheuristic Approach to Protein Structure Prediction$92531948
997   $aUNINA