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Bioinformatics for Cancer Immunotherapy

Overview of attention for book
Cover of 'Bioinformatics for Cancer Immunotherapy'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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 13: Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires
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Chapter title
Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires
Chapter number 13
Book title
Bioinformatics for Cancer Immunotherapy
Published in
Methods in molecular biology, March 2020
DOI 10.1007/978-1-0716-0327-7_13
Pubmed ID
Book ISBNs
978-1-07-160326-0, 978-1-07-160327-7
Authors

Sofie Gielis, Pieter Moris, Wout Bittremieux, Nicolas De Neuter, Benson Ogunjimi, Kris Laukens, Pieter Meysman, Gielis, Sofie, Moris, Pieter, Bittremieux, Wout, Neuter, Nicolas, Ogunjimi, Benson, Laukens, Kris, Meysman, Pieter, Neuter, Nicolas De

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Master 2 22%
Researcher 1 11%
Unknown 3 33%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Computer Science 1 11%
Medicine and Dentistry 1 11%
Unknown 5 56%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 February 2021.
All research outputs
#20,608,970
of 23,197,711 outputs
Outputs from Methods in molecular biology
#10,053
of 13,301 outputs
Outputs of similar age
#306,528
of 360,791 outputs
Outputs of similar age from Methods in molecular biology
#43
of 57 outputs
Altmetric has tracked 23,197,711 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,301 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 360,791 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.