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008			200302s2020 xxu| o |||| 0|eng d
020			_a9781071603277
024	7		_a10.1007/978-1-0716-0327-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC271 .I45 _b2020 EB
245	0	0	_aBioinformatics for Cancer Immunotherapy : _bMethods and Protocols _cedited by Sebastian Boegel
250			_a1st edition 2020
264		1	_aNew York, NY _bSpringer International Publising _c2020
300			_a1 recurso en línea (XII, 304 páginas) _b57 ilustraciones, 48 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 _v2120
505	0		_aBioinformatics for Cancer Immunotherapy -- An Individualized Approach for Somatic Variant Discovery -- Ensemble-Based Somatic Mutation Calling in Cancer Genomes -- SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations -- HLA Typing from RNA Sequencing and Applications to Cancer -- Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing -- HLApers: HLA Typing and Quantification of Expression with Personalized Index -- High-Throughput MHC I Ligand Prediction using MHCflurry -- In Silico Prediction of Tumor Neoantigens with TIminer -- OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction -- Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data -- The SysteMHC Atlas: A Computational Pipeline, A Website, and A Data Repository for Immunopeptidomics Analysis -- Identification of Epitope-Specific T Cells in T Cell Receptor Repertoires -- Modeling and Viewing T Cell Receptors using TCRmodel and TCR3d -- In Silico Cell Type Deconvolution Methods in Cancer Immunotherapy -- Immunedeconv - An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA Sequencing Data -- EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data -- Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data -- Cell Type Enrichment Analysis of Bulk Transcriptomes using xCell -- Cap Analysis of Gene Expression (CAGE), A Quantitative and Genome-Wide Assay of Transcription Start Sites.
520			_aThis volume focuses on a variety of in silico protocols of the latest bioinformatics tools and computational pipelines developed for neo-antigen identification and immune cell analysis from high-throughput sequencing data for cancer immunotherapy. The chapters in this book cover topics that discuss the two emerging concepts in recognition of tumor cells using endogenous T cells: cancer vaccines against neo-antigens presented on HLA class I and II alleles, and checkpoint inhibitors. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and authoritative, Bioinformatics for Cancer Immunotherapy: Methods and Protocols is a valuable research tool for any scientist and researcher interested in learning more about this exciting and developing field.
988			_aSpringer_Protocols_2020
650		7	_2embne _9348509 _aCáncer _xInmunoterapia
650		7	_2embne _9160489 _aBioinformática
776	0	8	_iPrinted edition: _z9781071603260
776	0	8	_iPrinted edition: _z9781071603284
776	0	8	_iPrinted edition: _z9781071603291
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-0327-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI