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Gene Function Analysis

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Cover of 'Gene Function Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The Present State and Future Direction of Integrated Gene Function Analysis
  3. Altmetric Badge
    Chapter 2 Performing Integrative Functional Genomics Analysis in GeneWeaver.org
  4. Altmetric Badge
    Chapter 3 Functional Annotation of Differentially Regulated Gene Set Using WebGestalt: A Gene Set Predictive of Response to Ipilimumab in Tumor Biopsies.
  5. Altmetric Badge
    Chapter 4 Integrative Data-Mining Tools to Link Gene and Function
  6. Altmetric Badge
    Chapter 5 Detection of Driver Protein Complexes in Breast Cancer Metastasis by Large-Scale Transcriptome–Interactome Integration
  7. Altmetric Badge
    Chapter 6 Pattern Identification in Time-Course Gene Expression Data with the CoGAPS Matrix Factorization.
  8. Altmetric Badge
    Chapter 7 Statistical Tools and R Software for Cancer Driver Probabilities
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    Chapter 8 Predicting the functional consequences of somatic missense mutations found in tumors.
  10. Altmetric Badge
    Chapter 9 Determining the effect of DNA methylation on gene expression in cancer cells.
  11. Altmetric Badge
    Chapter 10 Reverse Engineering Transcriptional Gene Networks
  12. Altmetric Badge
    Chapter 11 Integrating in silico resources to map a signaling network.
  13. Altmetric Badge
    Chapter 12 A method for inducible gene over-expression and down-regulation in emerging model species using pogostick.
  14. Altmetric Badge
    Chapter 13 Construction and application of site-specific artificial nucleases for targeted gene editing.
  15. Altmetric Badge
    Chapter 14 Selection of Recombinant Antibodies from Antibody Gene Libraries
  16. Altmetric Badge
    Chapter 15 Construction of Simple and Efficient siRNA Validation Systems for Screening and Identification of Effective RNAi-Targeted Sequences from Mammalian Genes
  17. Altmetric Badge
    Chapter 16 Rapid Genetic Modification of Mouse Embryonic Stem Cells by Inducible Cassette Exchange Recombination
  18. Altmetric Badge
    Chapter 17 In Ovo Electroporation of miRNA-Based-Plasmids to Investigate Gene Function in the Developing Neural Tube
  19. Altmetric Badge
    Chapter 18 Proteomic Strategies: SILAC and 2D-DIGE—Powerful Tool to Investigate Cellular Alterations
  20. Altmetric Badge
    Chapter 19 Conditional gene-trap mutagenesis in zebrafish.
Attention for Chapter 11: Integrating in silico resources to map a signaling network.
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Chapter title
Integrating in silico resources to map a signaling network.
Chapter number 11
Book title
Gene Function Analysis
Published in
Methods in molecular biology, November 2013
DOI 10.1007/978-1-62703-721-1_11
Pubmed ID
Book ISBNs
978-1-62703-720-4, 978-1-62703-721-1
Authors

Liu H, Beck TN, Golemis EA, Serebriiskii IG, Hanqing Liu, Tim N. Beck, Erica A. Golemis, Ilya G. Serebriiskii

Abstract

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 32%
Student > Bachelor 4 13%
Student > Ph. D. Student 4 13%
Professor > Associate Professor 3 10%
Other 2 6%
Other 5 16%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 26%
Biochemistry, Genetics and Molecular Biology 4 13%
Medicine and Dentistry 4 13%
Mathematics 1 3%
Computer Science 1 3%
Other 4 13%
Unknown 9 29%
Attention Score in Context

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 26 June 2014.
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#20,231,820
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Outputs from Methods in molecular biology
#9,864
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#262,762
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Outputs of similar age from Methods in molecular biology
#395
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