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Bioinformatics for Cancer Immunotherapy

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Cover of 'Bioinformatics for Cancer Immunotherapy'

Table of Contents

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    Book Overview
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    Chapter 1 Bioinformatics for Cancer Immunotherapy
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    Chapter 2 An Individualized Approach for Somatic Variant Discovery
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    Chapter 3 Ensemble-Based Somatic Mutation Calling in Cancer Genomes
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    Chapter 4 SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations
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    Chapter 5 HLA Typing from RNA Sequencing and Applications to Cancer
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    Chapter 6 Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing
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    Chapter 7 HLApers: HLA Typing and Quantification of Expression with Personalized Index
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    Chapter 8 High-Throughput MHC I Ligand Prediction Using MHCflurry
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    Chapter 9 In Silico Prediction of Tumor Neoantigens with TIminer
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    Chapter 10 OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction
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    Chapter 11 Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data
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    Chapter 12 The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses
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    Chapter 13 Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires
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    Chapter 14 Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d
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    Chapter 15 In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy
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    Chapter 16 Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data
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    Chapter 17 EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data
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    Chapter 18 Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data
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    Chapter 19 Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell
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    Chapter 20 Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites
Attention for Chapter 11: Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data
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Chapter title
Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data
Chapter number 11
Book title
Bioinformatics for Cancer Immunotherapy
Published by
Humana, New York, NY, March 2020
DOI 10.1007/978-1-0716-0327-7_11
Pubmed ID
Book ISBNs
978-1-07-160326-0, 978-1-07-160327-7
Authors

Prathyusha Konda, J. Patrick Murphy, Shashi Gujar

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