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Cancer Systems Biology

Overview of attention for book
Cover of 'Cancer Systems Biology'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis
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    Chapter 2 Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data
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    Chapter 3 Analyzing DNA Methylation Patterns During Tumor Evolution
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    Chapter 4 MicroRNA Networks in Breast Cancer Cells
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    Chapter 5 Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels
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    Chapter 6 Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells
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    Chapter 7 Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research
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    Chapter 8 Quantitative Analysis of Tyrosine Kinase Signaling Across Differentially Embedded Human Glioblastoma Tumors
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    Chapter 9 Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles
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    Chapter 10 Stochastic and Deterministic Models for the Metastatic Emission Process: Formalisms and Crosslinks
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    Chapter 11 Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details
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    Chapter 12 Profiling Tumor Infiltrating Immune Cells with CIBERSORT
  14. Altmetric Badge
    Chapter 13 Systems Biology Approaches in Cancer Pathology
  15. Altmetric Badge
    Chapter 14 Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing
  16. Altmetric Badge
    Chapter 15 A Robust Optimization Approach to Cancer Treatment under Toxicity Uncertainty
  17. Altmetric Badge
    Chapter 16 Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response
  18. Altmetric Badge
    Chapter 17 Methods for High-throughput Drug Combination Screening and Synergy Scoring
Attention for Chapter 12: Profiling Tumor Infiltrating Immune Cells with CIBERSORT
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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2 news outlets
blogs
1 blog
twitter
7 X users
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

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17 Dimensions

Readers on

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685 Mendeley
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Chapter title
Profiling Tumor Infiltrating Immune Cells with CIBERSORT
Chapter number 12
Book title
Cancer Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7493-1_12
Pubmed ID
Book ISBNs
978-1-4939-7492-4, 978-1-4939-7493-1
Authors

Binbin Chen, Michael S. Khodadoust, Chih Long Liu, Aaron M. Newman, Ash A. Alizadeh, Chen, Binbin, Khodadoust, Michael S., Liu, Chih Long, Newman, Aaron M., Alizadeh, Ash A.

Abstract

Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users 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 685 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 685 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 125 18%
Researcher 90 13%
Student > Master 74 11%
Student > Bachelor 56 8%
Student > Doctoral Student 36 5%
Other 92 13%
Unknown 212 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 185 27%
Medicine and Dentistry 69 10%
Agricultural and Biological Sciences 66 10%
Immunology and Microbiology 42 6%
Computer Science 26 4%
Other 61 9%
Unknown 236 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 30 October 2023.
All research outputs
#1,266,476
of 25,837,817 outputs
Outputs from Methods in molecular biology
#126
of 14,362 outputs
Outputs of similar age
#28,600
of 453,192 outputs
Outputs of similar age from Methods in molecular biology
#7
of 1,488 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,362 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 99% 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 453,192 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1,488 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 99% of its contemporaries.