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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
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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
  9. Altmetric Badge
    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
  11. Altmetric Badge
    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 5: Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Citations

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Chapter title
Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels
Chapter number 5
Book title
Cancer Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7493-1_5
Pubmed ID
Book ISBNs
978-1-4939-7492-4, 978-1-4939-7493-1
Authors

James Campbell, Colm J. Ryan, Christopher J. Lord

Abstract

Loss-of-function screening using RNA interference or CRISPR approaches can be used to identify genes that specific tumor cell lines depend upon for survival. By integrating the results from screens in multiple cell lines with molecular profiling data, it is possible to associate the dependence upon specific genes with particular molecular features (e.g., the mutation of a cancer driver gene, or transcriptional or proteomic signature). Here, using a panel of kinome-wide siRNA screens in osteosarcoma cell lines as an example, we describe a computational protocol for analyzing loss-of-function screens to identify genetic dependencies associated with particular molecular features. We describe the steps required to process the siRNA screen data, integrate the results with genotypic information to identify genetic dependencies, and finally the integration of protein-protein interaction data to interpret these dependencies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Student > Master 3 23%
Student > Bachelor 2 15%
Student > Doctoral Student 1 8%
Researcher 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 38%
Medicine and Dentistry 3 23%
Agricultural and Biological Sciences 3 23%
Computer Science 1 8%
Unknown 1 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 January 2018.
All research outputs
#13,068,996
of 23,031,582 outputs
Outputs from Methods in molecular biology
#3,319
of 13,177 outputs
Outputs of similar age
#207,254
of 442,381 outputs
Outputs of similar age from Methods in molecular biology
#283
of 1,499 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,177 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 73% 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 442,381 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.