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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
  3. Altmetric Badge
    Chapter 2 Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data
  4. Altmetric Badge
    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
  9. Altmetric Badge
    Chapter 8 Quantitative Analysis of Tyrosine Kinase Signaling Across Differentially Embedded Human Glioblastoma Tumors
  10. Altmetric Badge
    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
  12. Altmetric Badge
    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 3: Analyzing DNA Methylation Patterns During Tumor Evolution
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Chapter title
Analyzing DNA Methylation Patterns During Tumor Evolution
Chapter number 3
Book title
Cancer Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7493-1_3
Pubmed ID
Book ISBNs
978-1-4939-7492-4, 978-1-4939-7493-1
Authors

Heng Pan, Olivier Elemento

Abstract

Epigenetic modifications play a key role in cellular development and tumorigenesis. Recent large-scale genomic studies have shown that mutations in players of the epigenetic machinery and concomitant perturbation of epigenomic patterning are frequent events in tumors. Among epigenetic marks, DNA methylation is one of the best studied. Hyper- and hypo-methylation events of specific regulatory elements (such as promoters and enhancers) are sometimes thought to be correlated with expression of nearby genes. High-throughput bisulfite converted sequencing is currently the technology of choice for studying DNA methylation in base-pair resolution and on whole-genome scale. Such broad and high-resolution coverage investigations of the epigenome provide unprecedented opportunities to analyze DNA methylation patterns, which are correlated with tumorigenesis, tumor evolution, and tumor progression. However, few computational pipelines are available to the public to perform systematic DNA methylation analysis. Utilizing open source tools, we here describe a comprehensive computational methodology to thoroughly analyze DNA methylation patterns during tumor evolution based on bisulfite converted sequencing data, including intra-tumor methylation heterogeneity.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 24%
Student > Ph. D. Student 6 24%
Student > Master 5 20%
Student > Bachelor 2 8%
Student > Doctoral Student 1 4%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 28%
Medicine and Dentistry 6 24%
Chemical Engineering 2 8%
Agricultural and Biological Sciences 1 4%
Computer Science 1 4%
Other 5 20%
Unknown 3 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 January 2018.
All research outputs
#14,964,325
of 23,016,919 outputs
Outputs from Methods in molecular biology
#4,729
of 13,165 outputs
Outputs of similar age
#255,746
of 442,354 outputs
Outputs of similar age from Methods in molecular biology
#508
of 1,498 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,165 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% 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 442,354 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 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 60% of its contemporaries.