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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Overview of attention for book
Cover of 'Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification
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    Chapter 2 Using Ant Colony Optimization-Based Selected Features for Predicting Post-synaptic Activity in Proteins
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    Chapter 3 Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA
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    Chapter 4 DEEPER: A Full Parsing Based Approach to Protein Relation Extraction
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    Chapter 5 Improving the Performance of Hierarchical Classification with Swarm Intelligence
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    Chapter 6 Protein Interaction Inference Using Particle Swarm Optimization Algorithm
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    Chapter 7 Divide, Align and Full-Search for Discovering Conserved Protein Complexes
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    Chapter 8 Detection of Quantitative Trait Associated Genes Using Cluster Analysis
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    Chapter 9 Frequent Subsplit Representation of Leaf-Labelled Trees
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    Chapter 10 Inference on Missing Values in Genetic Networks Using High-Throughput Data
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    Chapter 11 Mining Gene Expression Patterns for the Discovery of Overlapping Clusters
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    Chapter 12 Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases
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    Chapter 13 Gene Selection and Cancer Microarray Data Classification Via Mixed-Integer Optimization
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    Chapter 14 Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs
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    Chapter 15 Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control
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    Chapter 16 Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps
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    Chapter 17 A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing
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    Chapter 18 On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways
Attention for Chapter 3: Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
32 Mendeley
citeulike
2 CiteULike
Summary Blogs Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA
Chapter number 3
Book title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published in
Lecture notes in computer science, January 2008
DOI 10.1007/978-3-540-78757-0_3
Pubmed ID
19779634
Book ISBNs
978-3-54-078756-3, 978-3-54-078757-0
Authors

Todd L. Edwards, William S. Bush, Stephen D. Turner, Scott M. Dudek, Eric S. Torstenson, Mike Schmidt, Eden Martin, Marylyn D. Ritchie, Edwards, Todd L., Bush, William S., Turner, Stephen D., Dudek, Scott M., Torstenson, Eric S., Schmidt, Mike, Martin, Eden, Ritchie, Marylyn D.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
Canada 1 3%
Unknown 29 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 8 25%
Professor > Associate Professor 4 13%
Student > Bachelor 3 9%
Student > Master 2 6%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 34%
Computer Science 8 25%
Biochemistry, Genetics and Molecular Biology 3 9%
Medicine and Dentistry 2 6%
Business, Management and Accounting 1 3%
Other 4 13%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 March 2013.
All research outputs
#5,716,386
of 22,703,044 outputs
Outputs from Lecture notes in computer science
#1,872
of 8,124 outputs
Outputs of similar age
#31,940
of 155,896 outputs
Outputs of similar age from Lecture notes in computer science
#29
of 85 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 76% 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 155,896 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 85 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 65% of its contemporaries.

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