↓ Skip to main content
What is this page? Embed badge

Bioinformatics for Cancer Immunotherapy

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

Table of Contents

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

Mentioned by

video
2 YouTube creators

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
105 Mendeley
Summary YouTube Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Bioinformatics for Cancer Immunotherapy
Chapter number 1
Book title
Bioinformatics for Cancer Immunotherapy
Published in
Methods in molecular biology, March 2020
DOI 10.1007/978-1-0716-0327-7_1
Pubmed ID
32124308
Book ISBNs
978-1-07-160326-0, 978-1-07-160327-7
Authors

Christoph Holtsträter, Barbara Schrörs, Thomas Bukur, Martin Löwer, Holtsträter, Christoph, Schrörs, Barbara, Bukur, Thomas, Löwer, Martin

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 15%
Researcher 16 15%
Student > Master 15 14%
Student > Doctoral Student 7 7%
Other 7 7%
Other 20 19%
Unknown 24 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 26%
Agricultural and Biological Sciences 14 13%
Immunology and Microbiology 9 9%
Medicine and Dentistry 9 9%
Computer Science 5 5%
Other 19 18%
Unknown 22 21%
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 02 May 2023.
All research outputs
#19,099,311
of 23,666,107 outputs
Outputs from Methods in molecular biology
#8,193
of 13,341 outputs
Outputs of similar age
#270,972
of 362,336 outputs
Outputs of similar age from Methods in molecular biology
#34
of 58 outputs
Altmetric has tracked 23,666,107 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,341 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 23rd percentile – i.e., 23% 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 362,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.

This page is provided by Altmetric.