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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
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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 17: EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
10 X users
patent
2 patents

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
96 Mendeley
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Chapter title
EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data
Chapter number 17
Book title
Bioinformatics for Cancer Immunotherapy
Published in
Methods in molecular biology, March 2020
DOI 10.1007/978-1-0716-0327-7_17
Pubmed ID
Book ISBNs
978-1-07-160326-0, 978-1-07-160327-7
Authors

Julien Racle, David Gfeller, Racle, Julien, Gfeller, David

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 17%
Student > Ph. D. Student 13 14%
Student > Master 9 9%
Other 6 6%
Student > Bachelor 5 5%
Other 17 18%
Unknown 30 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 29%
Immunology and Microbiology 7 7%
Medicine and Dentistry 5 5%
Computer Science 5 5%
Agricultural and Biological Sciences 5 5%
Other 8 8%
Unknown 38 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 23 May 2024.
All research outputs
#1,962,636
of 23,577,761 outputs
Outputs from Methods in molecular biology
#285
of 13,423 outputs
Outputs of similar age
#46,046
of 362,241 outputs
Outputs of similar age from Methods in molecular biology
#2
of 58 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,423 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 97% of its peers.
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 362,241 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
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 has done particularly well, scoring higher than 96% of its contemporaries.