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Cancer Gene Networks

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

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction: Cancer Gene Networks.
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    Chapter 2 Emerging Methods in Chemoproteomics with Relevance to Drug Discovery.
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    Chapter 3 ANXA7-GTPase as Tumor Suppressor: Mechanisms and Therapeutic Opportunities.
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    Chapter 4 Experimental and Study Design Considerations for Uncovering Oncometabolites.
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    Chapter 5 Targeting Deubiquitinating Enzymes and Autophagy in Cancer.
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    Chapter 6 Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution.
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    Chapter 7 Transcriptome and Proteome Analyses of TNFAIP8 Knockdown Cancer Cells Reveal New Insights into Molecular Determinants of Cell Survival and Tumor Progression.
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    Chapter 8 Network-Oriented Approaches to Anticancer Drug Response.
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    Chapter 9 CRISPR/Cas-Mediated Knockin in Human Pluripotent Stem Cells.
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    Chapter 10 Complete Transcriptome RNA-Seq.
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    Chapter 11 Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors.
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    Chapter 12 Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma.
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    Chapter 13 microRNA Target Prediction.
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    Chapter 14 Evaluating the Delivery of Proteins to the Cytosol of Mammalian Cells.
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    Chapter 15 Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.
  17. Altmetric Badge
    Chapter 16 Chemical Synthesis of Activity-Based Diubiquitin Probes.
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    Chapter 17 Profiling the Dual Enzymatic Activities of the Serine/Threonine Kinase IRE1α.
Attention for Chapter 1: Introduction: Cancer Gene Networks.
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Chapter title
Introduction: Cancer Gene Networks.
Chapter number 1
Book title
Cancer Gene Networks
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6539-7_1
Pubmed ID
Book ISBNs
978-1-4939-6537-3, 978-1-4939-6539-7
Authors

Robert Clarke

Editors

Usha Kasid, Robert Clarke

Abstract

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary differential equations and related tools to create dynamic, semi-mechanistic models of low dimensional data including gene/protein signaling as a function of time/dose. More recently, the integration of imaging technologies into predictive multiscale modeling has begun to extend further the scales across which data can be obtained and used to gain insight into system function.There are several goals for predictive multiscale modeling including the more academic pursuit of understanding how the system or local feature thereof is regulated or functions, to the more practical or translational goals of identifying predictive (selecting which patient should receive which drug/therapy) or prognostic (disease progress and outcome in an individual patient) biomarkers and/or identifying network vulnerabilities that represent potential targets for therapeutic benefit with existing drugs (including drug repurposing) or for the development of new drugs. These various goals are not necessarily mutually exclusive or inclusive. Within this volume, readers will find examples of many of the activities noted above. Each chapter contains practical and/or methodological insights to guide readers in the design and interpretation of their own and published work.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 2 15%
Student > Bachelor 2 15%
Professor 1 8%
Other 1 8%
Other 3 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 31%
Medicine and Dentistry 3 23%
Agricultural and Biological Sciences 1 8%
Immunology and Microbiology 1 8%
Unspecified 1 8%
Other 1 8%
Unknown 2 15%

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 17 January 2018.
All research outputs
#9,910,822
of 12,379,409 outputs
Outputs from Methods in molecular biology
#4,191
of 8,327 outputs
Outputs of similar age
#191,999
of 273,137 outputs
Outputs of similar age from Methods in molecular biology
#24
of 55 outputs
Altmetric has tracked 12,379,409 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,327 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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