↓ Skip to main content

Cancer Gene Networks

Overview of attention for book
Cover of 'Cancer Gene Networks'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction: Cancer Gene Networks.
  3. Altmetric Badge
    Chapter 2 Emerging Methods in Chemoproteomics with Relevance to Drug Discovery.
  4. Altmetric Badge
    Chapter 3 ANXA7-GTPase as Tumor Suppressor: Mechanisms and Therapeutic Opportunities.
  5. Altmetric Badge
    Chapter 4 Experimental and Study Design Considerations for Uncovering Oncometabolites.
  6. Altmetric Badge
    Chapter 5 Targeting Deubiquitinating Enzymes and Autophagy in Cancer.
  7. Altmetric Badge
    Chapter 6 Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution.
  8. Altmetric Badge
    Chapter 7 Transcriptome and Proteome Analyses of TNFAIP8 Knockdown Cancer Cells Reveal New Insights into Molecular Determinants of Cell Survival and Tumor Progression.
  9. Altmetric Badge
    Chapter 8 Network-Oriented Approaches to Anticancer Drug Response.
  10. Altmetric Badge
    Chapter 9 CRISPR/Cas-Mediated Knockin in Human Pluripotent Stem Cells.
  11. Altmetric Badge
    Chapter 10 Complete Transcriptome RNA-Seq.
  12. Altmetric Badge
    Chapter 11 Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors.
  13. Altmetric Badge
    Chapter 12 Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma.
  14. Altmetric Badge
    Chapter 13 microRNA Target Prediction.
  15. Altmetric Badge
    Chapter 14 Evaluating the Delivery of Proteins to the Cytosol of Mammalian Cells.
  16. Altmetric Badge
    Chapter 15 Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.
  17. Altmetric Badge
    Chapter 16 Chemical Synthesis of Activity-Based Diubiquitin Probes.
  18. Altmetric Badge
    Chapter 17 Profiling the Dual Enzymatic Activities of the Serine/Threonine Kinase IRE1α.
Attention for Chapter 15: Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

2 tweeters
1 Facebook page


1 Dimensions

Readers on

7 Mendeley
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
Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.
Chapter number 15
Book title
Cancer Gene Networks
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6539-7_15
Pubmed ID
Book ISBNs
978-1-4939-6537-3, 978-1-4939-6539-7

Catherine Jozwik, Ofer Eidelman, Joshua Starr, Harvey B. Pollard, Meera Srivastava


Usha Kasid, Robert Clarke


Genomics has revolutionized large-scale and high-throughput sequencing and has led to the discovery of thousands of new proteins. Protein chip technology is emerging as a miniaturized and highly parallel platform that is suited to rapid, simultaneous screening of large numbers of proteins and the analysis of various protein-binding activities, enzyme substrate relationships, and posttranslational modifications. Specifically, reverse capture protein microarrays provide the most appropriate platform for identifying low-abundance, disease-specific biomarker proteins in a sea of high-abundance proteins from biological fluids such as blood, serum, plasma, saliva, urine, and cerebrospinal fluid as well as tissues and cells obtained by biopsy. Samples from hundreds of patients can be spotted in serial dilutions on many replicate glass slides. Each slide can then be probed with one specific antibody to the biomarker of interest. That antibody's titer can then be determined quantitatively for each patient, allowing for the statistical assessment and validation of the diagnostic or prognostic utility of that particular antigen. As the technology matures and the availability of validated, platform-compatible antibodies increases, the platform will move further into the desirable realm of discovery science for detecting and quantitating low-abundance signaling proteins. In this chapter, we describe methods for the successful application of the reverse capture protein microarray platform for which we have made substantial contributions to the development and application of this method, particularly in the use of body fluids other than serum/plasma.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 43%
Student > Ph. D. Student 1 14%
Student > Bachelor 1 14%
Professor 1 14%
Unknown 1 14%
Readers by discipline Count As %
Medicine and Dentistry 2 29%
Biochemistry, Genetics and Molecular Biology 2 29%
Pharmacology, Toxicology and Pharmaceutical Science 1 14%
Unknown 2 29%

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 13 January 2018.
All research outputs
of 12,358,022 outputs
Outputs from Methods in molecular biology
of 8,323 outputs
Outputs of similar age
of 270,765 outputs
Outputs of similar age from Methods in molecular biology
of 55 outputs
Altmetric has tracked 12,358,022 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,323 research outputs from this source. They receive a mean Attention Score of 2.1. This one has gotten more attention than average, scoring higher than 56% 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 270,765 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.