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Protein Function Prediction

Overview of attention for book
Cover of 'Protein Function Prediction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Using PFP and ESG Protein Function Prediction Web Servers
  3. Altmetric Badge
    Chapter 2 GHOSTX: A Fast Sequence Homology Search Tool for Functional Annotation of Metagenomic Data
  4. Altmetric Badge
    Chapter 3 From Gene Annotation to Function Prediction for Metagenomics
  5. Altmetric Badge
    Chapter 4 An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS
  6. Altmetric Badge
    Chapter 5 MPFit: Computational Tool for Predicting Moonlighting Proteins
  7. Altmetric Badge
    Chapter 6 Predicting Secretory Proteins with SignalP
  8. Altmetric Badge
    Chapter 7 The ProFunc Function Prediction Server
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    Chapter 8 G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures
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    Chapter 9 Local Alignment of Ligand Binding Sites in Proteins for Polypharmacology and Drug Repositioning
  11. Altmetric Badge
    Chapter 10 WATsite2.0 with PyMOL Plugin: Hydration Site Prediction and Visualization
  12. Altmetric Badge
    Chapter 11 Enzyme Annotation and Metabolic Reconstruction Using KEGG
  13. Altmetric Badge
    Chapter 12 Ortholog Identification and Comparative Analysis of Microbial Genomes Using MBGD and RECOG
  14. Altmetric Badge
    Chapter 13 Exploring Protein Function Using the Saccharomyces Genome Database
  15. Altmetric Badge
    Chapter 14 Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server
  16. Altmetric Badge
    Chapter 15 The FANTOM5 Computation Ecosystem: Genomic Information Hub for Promoters and Active Enhancers
  17. Altmetric Badge
    Chapter 16 Multi-Algorithm Particle Simulations with Spatiocyte
Attention for Chapter 14: Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server
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Chapter title
Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server
Chapter number 14
Book title
Protein Function Prediction
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-7015-5_14
Pubmed ID
28451980
Book ISBNs
978-1-4939-7013-1, 978-1-4939-7015-5, 978-1-4939-7013-1, 978-1-4939-7015-5
Authors

Eiru Kim, Insuk Lee, Kim, Eiru, Lee, Insuk

Editors

Daisuke Kihara

Abstract

The mouse, Mus musculus, is a popular model organism for the study of human genes involved in development, immunology, and disease phenotypes. Despite recent revolutions in gene-knockout technologies in mouse, identification of candidate genes for functions of interest can further accelerate the discovery of novel gene functions. The collaborative nature of genetic functions allows for the inference of gene functions based on the principle of guilt-by-association. Genome-scale co-functional networks could therefore provide functional predictions for genes via network analysis. We recently constructed such a network for mouse (MouseNet), which interconnects over 88% of protein-coding genes with 788,080 functional relationships. The companion web server ( www.inetbio.org/mousenet ) enables researchers with no bioinformatics expertise to generate predictions that facilitate discovery of novel gene functions. In this chapter, we present the theoretical framework for MouseNet, as well as step-by-step instructions and technical tips for functional prediction of genes and pathways in mouse and other model vertebrates.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Researcher 1 25%
Unknown 1 25%
Readers by discipline Count As %
Computer Science 2 50%
Social Sciences 1 25%
Unknown 1 25%
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 14 August 2017.
All research outputs
#14,931,166
of 22,968,808 outputs
Outputs from Methods in molecular biology
#4,719
of 13,137 outputs
Outputs of similar age
#184,358
of 310,521 outputs
Outputs of similar age from Methods in molecular biology
#95
of 278 outputs
Altmetric has tracked 22,968,808 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 13,137 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 310,521 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 278 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.

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