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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
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    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
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    Chapter 11 Enzyme Annotation and Metabolic Reconstruction Using KEGG
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    Chapter 12 Ortholog Identification and Comparative Analysis of Microbial Genomes Using MBGD and RECOG
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    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 16: Multi-Algorithm Particle Simulations with Spatiocyte
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Chapter title
Multi-Algorithm Particle Simulations with Spatiocyte
Chapter number 16
Book title
Protein Function Prediction
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-7015-5_16
Pubmed ID
28451982
Book ISBNs
978-1-4939-7013-1, 978-1-4939-7015-5
Authors

Satya N. V. Arjunan, Koichi Takahashi

Editors

Daisuke Kihara

Abstract

As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

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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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Professor > Associate Professor 1 14%
Other 1 14%
Unknown 2 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 29%
Business, Management and Accounting 1 14%
Computer Science 1 14%
Agricultural and Biological Sciences 1 14%
Unknown 2 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 24 May 2017.
All research outputs
#20,418,183
of 22,968,808 outputs
Outputs from Methods in molecular biology
#9,923
of 13,137 outputs
Outputs of similar age
#270,141
of 310,521 outputs
Outputs of similar age from Methods in molecular biology
#227
of 278 outputs
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So far Altmetric has tracked 13,137 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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