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008			220613s2022 xxu| o |||| 0|eng d
020			_a9781071623176
024	7		_a10.1007/978-1-0716-2317-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH450 _b2022 EB
245	0	0	_aComputational Methods for Predicting Post-Translational Modification Sites _cedited by Dukka B. KC
250			_a1st edition 2022
264		1	_aNew York, NY _bSpringer International Publising _c2022
300			_a1 recurso en línea (XVII, 326 páginas) _b66 ilustraciones, 60 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 _v2499
505	0		_aMaximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling -- PLDMS: Phosphopeptide Library Dephosphorylation followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences -- FEPS: A tool for Feature Extraction from Protein Sequence -- A pre-trained ELECTRA model for Kinase-specific Phosphorylation Site Prediction -- iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using structural-based Features -- Functions of Glycosylation and Related Web Resources for its Prediction -- Analysis of Post-Translational Modifications in Arabidopsis Proteins and Metabolic Pathways using the FAT-PTM Database -- Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A RANDOM FOREST-BASED PREDICTOR and classifier for Prxs -- Computational prediction of N- and O-linked glycosylation sites for human and mouse proteins -- iPTMnet RESTful API for Post-Translational Modification Network Analysis -- Systematic Characterization of Lysine Post-Translational Modification Sites using MUscADEL -- Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation -- Exploration of Protein Post-Translational Modification Landscape and Crosstalk with CrossTalkMapper -- PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins -- Deep Learning-Based Advances In Protein Post-Translational Modification Site And Protein Cleavage Prediction.
520			_aThis volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Authoritative and cutting-edge, Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction. .
988			_aSpringer_Protocols_2022
650		7	_2embne _9138044 _aGenética _vManuales de laboratorio
776	0	8	_iPrinted edition: _z9781071623169
776	0	8	_iPrinted edition: _z9781071623183
776	0	8	_iPrinted edition: _z9781071623190
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-2317-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2023 _dz _eu _zSI