000			03276nam a22003735i 4500
001			102072
003			DE-He213
005			20240111050135.0
007			cr nn 008mamaa
008			180305s2018 gw | s |||| 0|eng d
020			_a9783319747750
024	7		_a10.1007/978-3-319-74775-0 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ342 _b2018 EB
100	1		_aJana, Nanda Dulal _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	2	_aA Metaheuristic Approach to Protein Structure Prediction _bAlgorithms and Insights from Fitness Landscape Analysis _cby Nanda Dulal Jana, Swagatam Das, Jaya Sil.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXIX, 220 páginas 59 ilustraciones, 54 ilustraciones a color)
347			_atext file _bPDF
490	0		_aEmergence, Complexity and Computation _x2194-7287 _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	3		_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		7	_9666321 _aIngeniería asistida por ordenador
650		7	_aInteligencia artificial _2embne _9413115
650		7	_aBioquímica _2embne _9138639
700	1		_aDas, Swagatam _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2009009215 _1http://viaf.org/viaf/86100097/ _998976
700	1		_aSil, Jaya _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://dbpedia.org/resource/Jaya_Seal
776	0	8	_iEdición impresa: _z9783319747743
776	0	8	_iEdición impresa: _z9783319747767
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-74775-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102072 _d102072 _x1