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008			210317s2021 xxu| s |||| 0|eng d
020			_a9781071609477
024	7		_a10.1007/978-1-0716-0947-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aEpistasis _bMethods and Protocols _cedited by Ka-Chun Wong.
250			_a1st edition 2021
264		1	_aNew York, NY _bSpringer International Publising _c2021
300			_a1 recurso en línea (X, 402 páginas) _b167 ilustraciones, 85 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v2212
505	0		_aMass-based Protein Phylogenetic Approach to Identify Epistasis -- SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration -- Epistasis-based Feature Selection Algorithm -- W-test for Genetic Epistasis Testing -- The Combined Analysis of Pleiotropy and Epistasis (CAPE) -- Two-Stage Testing for Epistasis: Screening and Veri_cation -- Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies -- Phenotype Prediction under Epistasis -- Simulating Evolution in Asexual Populations with Epistasis -- Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package -- Brief survey on Machine Learning in Epistasis -- First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions -- Gene-Environment Interaction: AVariable Selection Perspective -- Using C-JAMP to Investigate Epistasis and Pleiotropy -- Identifying the Significant Change of Gene Expression in Genomic Series Data -- Analyzing High-Order Epistasis from Genotype-phenotype Maps Using 'Epistasis' Package -- Deep Neural Networks for Epistatic Sequences Analysis -- Protocol for Epistasis Detection with Machine Learning Using GenEpi Package -- A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection -- Epistasis Detection Based on Epi-GTBN -- Epistasis Analysis: Classification through Machine Learning Methods -- Genetic Interaction Network Interpretation: A Tidy Data Science Perspective -- Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis.
520			_aThis volume explores methods and protocols for detecting epistasis from genetic data. Chapters provide methods and protocols demonstrating approaches to identify epistasis, genetic epistasis testing, genome-wide epistatic SNP networks, epistasis detection through machine learning, and complex interaction analysis using trigenic synthetic genetic array (τ-SGA). Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Epistasis: Methods and Protocols aims to ensure successful results in the further study of this vital field. .
700	1		_aWong, Ka-Chun _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781071609460
776	0	8	_iPrinted edition: _z9781071609484
776	0	8	_iPrinted edition: _z9781071609491
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-0947-7 _z(usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Protocols_2021
999			_c233235 _d233235