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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

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    Book Overview
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    Chapter 1 Variable Genetic Operator Search for the Molecular Docking Problem
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    Chapter 2 Role of Centrality in Network-Based Prioritization of Disease Genes
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    Chapter 3 Parallel Multi-Objective Approaches for Inferring Phylogenies
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    Chapter 4 An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction
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    Chapter 5 Finding Gapped Motifs by a Novel Evolutionary Algorithm
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    Chapter 6 Top-Down Induction of Phylogenetic Trees
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    Chapter 7 A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships
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    Chapter 8 Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci
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    Chapter 9 Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions
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    Chapter 10 Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques
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    Chapter 11 Correlation–Based Scatter Search for Discovering Biclusters from Gene Expression Data
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    Chapter 12 A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination
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    Chapter 13 A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins
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    Chapter 14 Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments
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    Chapter 15 Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models
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    Chapter 16 The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics
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    Chapter 17 Artificial Immune Systems for Epistasis Analysis in Human Genetics
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    Chapter 18 Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models
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    Chapter 19 Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study
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    Chapter 20 Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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    Chapter 21 Investigating Populational Evolutionary Algorithms to Add Vertical Meaning in Phylogenetic Trees
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
1 X user
wikipedia
1 Wikipedia page
video
1 YouTube creator

Readers on

mendeley
153 Mendeley
citeulike
2 CiteULike
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Title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published by
Lecture notes in computer science, April 2010
DOI 10.1007/978-3-642-12211-8
Pubmed ID
ISBNs
978-3-64-212210-1, 978-3-64-212211-8
Authors

Turner SD, Dudek SM, Ritchie MD

Editors

Pizzuti, Clara, Ritchie, Marylyn D., Giacobini, Mario

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

Geographical breakdown

Country Count As %
United States 3 2%
Iran, Islamic Republic of 2 1%
Germany 1 <1%
Italy 1 <1%
Poland 1 <1%
Brazil 1 <1%
Finland 1 <1%
Peru 1 <1%
Sweden 1 <1%
Other 13 8%
Unknown 128 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 33%
Student > Master 36 24%
Researcher 23 15%
Professor > Associate Professor 9 6%
Student > Postgraduate 8 5%
Other 26 17%
Readers by discipline Count As %
Computer Science 54 35%
Agricultural and Biological Sciences 41 27%
Engineering 13 8%
Biochemistry, Genetics and Molecular Biology 8 5%
Business, Management and Accounting 4 3%
Other 33 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 April 2018.
All research outputs
#6,876,800
of 23,041,514 outputs
Outputs from Lecture notes in computer science
#2,223
of 8,145 outputs
Outputs of similar age
#32,689
of 95,995 outputs
Outputs of similar age from Lecture notes in computer science
#4
of 18 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,145 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 72% 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 95,995 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.