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Cancer Gene Networks

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Cover of 'Cancer Gene Networks'

Table of Contents

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    Book Overview
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    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.
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    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 13: microRNA Target Prediction.
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Chapter title
microRNA Target Prediction.
Chapter number 13
Book title
Cancer Gene Networks
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6539-7_13
Pubmed ID
Book ISBNs
978-1-4939-6537-3, 978-1-4939-6539-7
Authors

William Ritchie

Editors

Usha Kasid, Robert Clarke

Abstract

microRNAs are short RNAs that reduce gene expression by binding to their targets. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Commonly used software will produce a prohibitive number of predicted targets for each microRNA. Here I describe procedures that refine these predictions by integrating available software and expression data from experiments available online. These procedures are tailored to experiments where predicting true targets is more important than detecting all putative targets.

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 1%
Argentina 1 1%
South Africa 1 1%
Unknown 62 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 34%
Student > Master 10 15%
Researcher 9 13%
Student > Postgraduate 6 9%
Student > Doctoral Student 4 6%
Other 10 15%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 48%
Biochemistry, Genetics and Molecular Biology 11 16%
Medicine and Dentistry 7 10%
Computer Science 6 9%
Physics and Astronomy 1 1%
Other 3 4%
Unknown 7 10%