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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
  21. Altmetric Badge
    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
Attention for Chapter 13: A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins
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Chapter title
A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins
Chapter number 13
Book title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published in
Lecture notes in computer science, January 2010
DOI 10.1007/978-3-642-12211-8_13
Book ISBNs
978-3-64-212210-1, 978-3-64-212211-8
Authors

Leonidas Kapsokalivas, Kathleen Steinhöfel, Kapsokalivas, Leonidas, Steinhöfel, Kathleen

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 20%
Italy 1 20%
Unknown 3 60%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 40%
Student > Ph. D. Student 1 20%
Other 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 4 80%
Unknown 1 20%
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 10 September 2010.
All research outputs
#20,194,368
of 22,711,645 outputs
Outputs from Lecture notes in computer science
#6,981
of 8,124 outputs
Outputs of similar age
#156,700
of 163,636 outputs
Outputs of similar age from Lecture notes in computer science
#158
of 189 outputs
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So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 189 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.